Journal articles: 'Data fusion algorithms'

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ZHU, SHANFENG, QIZHI FANG, and WEIMIN ZHENG. "SOCIAL CHOICE FOR DATA FUSION." International Journal of Information Technology & Decision Making 03, no. 04 (December 2004): 619–31. http://dx.doi.org/10.1142/s0219622004001288.

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Social choice theory is the study of decision theory on how to aggregate separate preferences into group's rational preference. It has wide applications, especially on the design of voting rules, and brings far-reaching influence on the development of modern political science and welfare economics. With the advent of the information age, social choice theory finds its up-to-date application on designing effective Metasearch engines. Metasearch engines provide effective searching by combining the results of multiple source search engines that make use of diverse models and techniques. In this work, we analyze social choice algorithms in a graph-theoretic approach. In addition to classical social choice algorithms, such as Borda and Condorcet, we study one special type of social choice algorithms, elimination voting, to tackle Metasearch problem. Some new algorithms are proposed and examined in the fusion experiment on TREC data. It shows that these elimination voting algorithms achieve satisfied performance when compared with Borda algorithm.

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Quadri, S. A., and Othman Sidek. "Role of Algorithm Engineering in Data Fusion Algorithms." Journal of Computational Intelligence and Electronic Systems 2, no. 1 (June 1, 2013): 29–35. http://dx.doi.org/10.1166/jcies.2013.1046.

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LIPOVETSKY, STAN. "DATA FUSION IN SEVERAL ALGORITHMS." Advances in Adaptive Data Analysis 05, no. 03 (July 2013): 1350014. http://dx.doi.org/10.1142/s1793536913500143.

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Data fusion consists of the process of integrating several datasets with some common variables, and other variables available only in partial datasets. The main problem of data fusion can be described as follows. From one source, having X0 and Y0 datasets (with N0 observations by multiple x and y variables, n and m of those, respectively), and from another source, having X1 data (with N1 observations by the same nx-variables), we need to estimate the missing portion of the Y1 data (of size N1 by m variables) in order to combine all the data into one set. Several algorithms are considered in this work, including estimation of weights proportional to the distances from each ith observation in the X1 "recipients" dataset to all observations in the X0 "donors" dataset. Or we can use a sample balancing technique with the maximum effective base performed by applying ridge-regression for the Gifi system of binaries obtained from the x-variables for the best fit of the "donors" X0 data to the margins defined by each respondent in the "recipients" X1 dataset. Then the weighted regressions of each y in the Y0 dataset by all variables in the X0 are constructed. For each ith observation in the dataset X0, these regressions are used for predicting the y-variables in the Y1 "recipients" dataset. If X and Y are the same n variables from different sources, the dual partial least squares technique and a special regression model with dummies defining each of the three available sets are used for prediction of the Y1 data.

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Zhang, Jie. "Security Technology of Wireless Sensor Internet of Things Based on Data Fusion." International Journal of Online Engineering (iJOE) 13, no. 11 (November 22, 2017): 25. http://dx.doi.org/10.3991/ijoe.v13i11.7748.

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<span style="font-family: 'Times New Roman',serif; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US">In order to prove the effect of data fusion technology in the Internet of things, a wireless sensor Internet of things security technology based on data fusion is designed, and the impact of data fusion in the field of communication technology is studied. Therefore, two security fusion algorithms are designed on the basis of analyzing and comparing the advantages and disadvantages of various security fusion algorithms, namely, data security fusion algorithm EDCSDA and approximate fusion algorithm PADSA. By analyzing the probability distribution model of the data collected by the nodes, the disturbance data is superimposed on the original data to hide the effect of the original data. A test bed system for perception layer of the Internet of things is designed and implemented. The test results prove the feasibility of the two algorithms. Meanwhile, it shows that the two algorithms can reduce the transmission overhead of the network while guaranteeing the security. Based on the above finding, it is concluded that data fusion technology is very effective for improving network efficiency and prolonging the network life cycle as one of the key technologies in the perception layer of Internet of things.</span>

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Tan, Yuxiang, Yann Tambouret, and Stefano Monti. "SimFuse: A Novel Fusion Simulator for RNA Sequencing (RNA-Seq) Data." BioMed Research International 2015 (2015): 1–5. http://dx.doi.org/10.1155/2015/780519.

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The performance evaluation of fusion detection algorithms from high-throughput sequencing data crucially relies on the availability of data with known positive and negative cases of gene rearrangements. The use of simulated data circumvents some shortcomings of real data by generation of an unlimited number of true and false positive events, and the consequent robust estimation of accuracy measures, such as precision and recall. Although a few simulated fusion datasets from RNA Sequencing (RNA-Seq) are available, they are of limited sample size. This makes it difficult to systematically evaluate the performance of RNA-Seq based fusion-detection algorithms. Here, we present SimFuse to address this problem. SimFuse utilizes real sequencing data as the fusions’ background to closely approximate the distribution of reads from a real sequencing library and uses a reference genome as the template from which to simulate fusions’ supporting reads. To assess the supporting read-specific performance, SimFuse generates multiple datasets with various numbers of fusion supporting reads. Compared to an extant simulated dataset, SimFuse gives users control over the supporting read features and the sample size of the simulated library, based on which the performance metrics needed for the validation and comparison of alternative fusion-detection algorithms can be rigorously estimated.

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Castanedo, Federico. "A Review of Data Fusion Techniques." Scientific World Journal 2013 (2013): 1–19. http://dx.doi.org/10.1155/2013/704504.

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The integration of data and knowledge from several sources is known as data fusion. This paper summarizes the state of the data fusion field and describes the most relevant studies. We first enumerate and explain different classification schemes for data fusion. Then, the most common algorithms are reviewed. These methods and algorithms are presented using three different categories: (i) data association, (ii) state estimation, and (iii) decision fusion.

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Abdulhafiz, Waleed A., and Alaa Khamis. "Handling Data Uncertainty and Inconsistency Using Multisensor Data Fusion." Advances in Artificial Intelligence 2013 (November 3, 2013): 1–11. http://dx.doi.org/10.1155/2013/241260.

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Data provided by sensors is always subjected to some level of uncertainty and inconsistency. Multisensor data fusion algorithms reduce the uncertainty by combining data from several sources. However, if these several sources provide inconsistent data, catastrophic fusion may occur where the performance of multisensor data fusion is significantly lower than the performance of each of the individual sensor. This paper presents an approach to multisensor data fusion in order to decrease data uncertainty with ability to identify and handle inconsistency. The proposed approach relies on combining a modified Bayesian fusion algorithm with Kalman filtering. Three different approaches, namely, prefiltering, postfiltering and pre-postfiltering are described based on how filtering is applied to the sensor data, to the fused data or both. A case study to find the position of a mobile robot by estimating its x and y coordinates using four sensors is presented. The simulations show that combining fusion with filtering helps in handling the problem of uncertainty and inconsistency of the data.

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Gan, Hock, Iosif Mporas, Saeid Safavi, and Reza Sotudeh. "Speaker Identification Using Data-Driven Score Classification." Image Processing & Communications 21, no. 2 (June 1, 2016): 55–63. http://dx.doi.org/10.1515/ipc-2016-0011.

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Abstract We present a comparative evaluation of different classification algorithms for a fusion engine that is used in a speaker identity selection task. The fusion engine combines the scores from a number of classifiers, which uses the GMM-UBM approach to match speaker identity. The performances of the evaluated classification algorithms were examined in both the text-dependent and text-independent operation modes. The experimental results indicated a significant improvement in terms of speaker identification accuracy, which was approximately 7% and 14.5% for the text-dependent and the text-independent scenarios, respectively. We suggest the use of fusion with a discriminative algorithm such as a Support Vector Machine in a real-world speaker identification application where the text-independent scenario predominates based on the findings.

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Yin, Hu, Yun Fei Lv, and Wei Wei Wang. "Reacher in Users Recommended of Social Data." Applied Mechanics and Materials 303-306 (February 2013): 2416–24. http://dx.doi.org/10.4028/www.scientific.net/amm.303-306.2416.

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We discuss some key techniques associated with integrating user social data recommendation into entity search engine, which can provide entity search engine more accurate information and make up for automatically fetching information on Web. The goal of social data recommendation is to make search engine become a content provider, and solve some challenges that traditional architecture of search engine has faced with, such as limited resources, accurate search, etc. To this end, we describe the storage format of the user social recommended data and submission methods for them. For the purpose of fusing this structural information into entity search engine, we present formal definitions related to Web entity fusion, and give several important fusion operators, and discuss their properties. Finally, we propose a Web entity fusion algorithm, which exploits some techniques related to natural language processing such as sentence similarity computation and sentence fusion. Our experimental results show that the proposed algorithms are effective.

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Shabanian, Mahdieh, and Seyed Hadi Hosseini. "Sensor Data Fusion Using Mutual Information Algorithm." Ciência e Natura 37 (December 19, 2015): 146. http://dx.doi.org/10.5902/2179460x20765.

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Traffic flow prediction is one of the congestion avoidance methods in highways. According to previous studies, no comprehensive model has been proposed for traffic flow prediction which can prevent congestion in many different traffic conditions. Using data fusion to reduce prediction error is an interesting idea to solve this problem. In this paper, a new hybrid algorithm based on mutual information for traffic flow prediction will be proposed and compared with various types of previous hybrid algorithms and predictors. The Mutual Information (MI) algorithm is used to calculate the interdependency of data, so we expect this new hybrid algorithm to have high precision in comparison with others. Simulations will be implemented based on real data in MATLAB environment as a performance demonstration of new hybrid algorithm. Due to variety of traffic flow, performance investigations of our new hybrid algorithm will be done in presence of polluted traffic data in different climatic conditions such as rain/snow fall or other traffic conditions like congestions and accidents on the road, indicating robustness of this algorithm to different types of noisy data

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Bancroft, Jared B., and Gérard Lachapelle. "Data Fusion Algorithms for Multiple Inertial Measurement Units." Sensors 11, no. 7 (June 29, 2011): 6771–98. http://dx.doi.org/10.3390/s110706771.

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Cătălin, NAE. "Disaster Monitoring using Grid Based Data Fusion Algorithms." INCAS BULLETIN 2, no. 4 (December 24, 2010): 143–52. http://dx.doi.org/10.13111/2066-8201.2010.2.4.19.

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Allerton, D. J., and H. Jia. "Distributed data fusion algorithms for inertial network systems." IET Radar, Sonar & Navigation 2, no. 1 (February 1, 2008): 51–62. http://dx.doi.org/10.1049/iet-rsn:20060159.

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Xia, Youshen, and Henry Leung. "Performance analysis of statistical optimal data fusion algorithms." Information Sciences 277 (September 2014): 808–24. http://dx.doi.org/10.1016/j.ins.2014.03.015.

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Li, Yueru. "Dance Motion Capture Based on Data Fusion Algorithm and Wearable Sensor Network." Complexity 2021 (June 23, 2021): 1–11. http://dx.doi.org/10.1155/2021/2656275.

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In this paper, through an in-depth study and analysis of dance motion capture algorithms in wearable sensor networks, the extended Kalman filter algorithm and the quaternion method are selected after analysing a variety of commonly used data fusion algorithms and pose solving algorithms. In this paper, a sensor-body coordinate system calibration algorithm based on hand-eye calibration is proposed, which only requires three calibration poses to complete the calibration of the whole-body sensor-body coordinate system. In this paper, joint parameter estimation algorithm based on human joint constraints and limb length estimation algorithm based on closed joint chains are proposed, respectively. The algorithm is an iterative optimization algorithm that divides each iteration into an expectation step and a great likelihood step, and the best convergence value can be found efficiently according to each iteration step. The feature values of each pose action are fed into the algorithm for model learning, which enables the training of the model. The trained model is then tested by combining the collected gesture data with the algorithmic model to recognize and classify the gesture data, observe its recognition accuracy, and continuously optimize the model to achieve accurate recognition of human gesture actions.

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Liu, Xiaofeng, Zhimin Feng, Yuehua Chen, and Hongwei Li. "Multiple optimized support vector regression for multi-sensor data fusion of weigh-in-motion system." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 234, no. 12 (June 7, 2020): 2807–21. http://dx.doi.org/10.1177/0954407020918802.

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Weigh-in-motion is an efficient way to manage overload vehicles, and usually utilizes multi-sensor to measure vehicle weight at present. To increase generalization and accuracy of support vector regression (SVR) applied in multi-sensor weigh-in-motion data fusion, three improved algorithms are presented in this paper. The first improved algorithm divides train samples into two sets to construct SVR1 and SVR2, respectively, and then test samples are distributed to SVR1 or SVR2 based on the nearest distance principle. The second improved algorithm calculates the theoretical biases of two training samples closeted to one test sample, and then obtains the bias of the test sample by linear interpolation method. The third improved algorithm utilizes the second improved algorithm to realize adaptive adjustment of biases for SVR1 and SVR2. Five vehicles were selected to conduct multi-sensor weigh-in-motion experiments on the built test platform. According to the obtained experiment data, fusion tests of SVR and three improved algorithms are performed, respectively. The results show that three improved algorithms gradually increase accuracy of SVR with fast operation speed, and the third improved algorithm exhibits the best application prospect in multi-sensor weigh-in-motion data fusion.

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Xue, Ying Hua, and Jing Li. "Distributed Information Fusion Structure Based on Data Fusion Tree." Advanced Materials Research 225-226 (April 2011): 488–91. http://dx.doi.org/10.4028/www.scientific.net/amr.225-226.488.

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A distributed information fusion structure based on data fusion tree is built to realize precise localization and efficient navigation for the mobile robot. The multi-class, multi-level information from robot and environment is fused using different algorithms in different levels, and make the robot have a deeper understanding to the whole environment. Experiments demonstrate that the new model proposed in the paper can improve the positioning precision of robot greatly, and the search efficiency and success rate are also better than traditional mode.

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Sheng, Xueli, Yang Chen, Longxiang Guo, Jingwei Yin, and Xiao Han. "Multitarget Tracking Algorithm Using Multiple GMPHD Filter Data Fusion for Sonar Networks." Sensors 18, no. 10 (September 21, 2018): 3193. http://dx.doi.org/10.3390/s18103193.

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Multitarget tracking algorithms based on sonar usually run into detection uncertainty, complex channel and more clutters, which cause lower detection probability, single sonar sensors failing to measure when the target is in an acoustic shadow zone, and computational bottlenecks. This paper proposes a novel tracking algorithm based on multisensor data fusion to solve the above problems. Firstly, under more clutters and lower detection probability condition, a Gaussian Mixture Probability Hypothesis Density (GMPHD) filter with computational advantages was used to get local estimations. Secondly, this paper provided a maximum-detection capability multitarget track fusion algorithm to deal with the problems caused by low detection probability and the target being in acoustic shadow zones. Lastly, a novel feedback algorithm was proposed to improve the GMPHD filter tracking performance, which fed the global estimations as a random finite set (RFS). In the end, the statistical characteristics of OSPA were used as evaluation criteria in Monte Carlo simulations, which showed this algorithm’s performance against those sonar tracking problems. When the detection probability is 0.7, compared with the GMPHD filter, the OSPA mean of two sensor and three sensor fusion was decrease almost by 40% and 55%, respectively. Moreover, this algorithm successfully tracks targets in acoustic shadow zones.

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Carrara, Matteo, Marco Beccuti, Fulvio Lazzarato, Federica Cavallo, Francesca Cordero, Susanna Donatelli, and Raffaele A. Calogero. "State-of-the-Art Fusion-Finder Algorithms Sensitivity and Specificity." BioMed Research International 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/340620.

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Background. Gene fusions arising from chromosomal translocations have been implicated in cancer. RNA-seq has the potential to discover such rearrangements generating functional proteins (chimera/fusion). Recently, many methods for chimeras detection have been published. However, specificity and sensitivity of those tools were not extensively investigated in a comparative way.Results. We tested eight fusion-detection tools (FusionHunter, FusionMap, FusionFinder, MapSplice, deFuse, Bellerophontes, ChimeraScan, and TopHat-fusion) to detect fusion events using synthetic and real datasets encompassing chimeras. The comparison analysis run only on synthetic data could generate misleading results since we found no counterpart on real dataset. Furthermore, most tools report a very high number of false positive chimeras. In particular, the most sensitive tool, ChimeraScan, reports a large number of false positives that we were able to significantly reduce by devising and applying two filters to remove fusions not supported by fusion junction-spanning reads or encompassing large intronic regions.Conclusions. The discordant results obtained using synthetic and real datasets suggest that synthetic datasets encompassing fusion events may not fully catch the complexity of RNA-seq experiment. Moreover, fusion detection tools are still limited in sensitivity or specificity; thus, there is space for further improvement in the fusion-finder algorithms.

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Mohite, Priya. "Adaptive Data Fusion for Energy Efficient Routing in Wireless Sensor Network." International Journal of Energy Optimization and Engineering 4, no. 1 (January 2015): 1–17. http://dx.doi.org/10.4018/ijeoe.2015010101.

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The data fusion process has led to an evolution for emerging Wireless Sensor Networks (WSNs) and examines the impact of various factors on energy consumption. Significantly there has always been a constant effort to enhance network efficiency without decreasing the quality of information. Based on Adaptive Fusion Steiner Tree (AFST), this paper proposes a heuristic algorithm called Modified Adaptive Fusion Steiner Tree (M-AFST) for energy efficient routing which not only does adaptively adjusts the information routes but also receives the required information from data sources and uses an extra buffer for backlogging incoming packets, so that the process of data fusion could be optimized by minimizing the overall data transmission. Experimental results prove the effectiveness of the proposed algorithm and achieve better performance than few existing algorithms discussed in the paper.

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Shivashankarappa, N., and Raol J. R. "Data Fusion Algorithms with State Delay and Missing Measurements." International Journal of Engineering Research and Applications 07, no. 06 (July 2017): 62–68. http://dx.doi.org/10.9790/9622-0706066268.

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Dong, Jiang, Dafang Zhuang, Yaohuan Huang, and Jingying Fu. "Advances in Multi-Sensor Data Fusion: Algorithms and Applications." Sensors 9, no. 10 (September 30, 2009): 7771–84. http://dx.doi.org/10.3390/s91007771.

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Miloslavov, Adelin, and Malathi Veeraraghavan. "Sensor Data Fusion Algorithms for Vehicular Cyber-Physical Systems." IEEE Transactions on Parallel and Distributed Systems 23, no. 9 (September 2012): 1762–74. http://dx.doi.org/10.1109/tpds.2012.107.

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Raol, J. R., and G. Girija. "Sensor data fusion algorithms using square-root information filtering." IEE Proceedings - Radar, Sonar and Navigation 149, no. 2 (2002): 89. http://dx.doi.org/10.1049/ip-rsn:20020128.

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Ghosh, Kripabandhu, Swapan Kumar Parui, and Prasenjit Majumder. "Learning combination weights in data fusion using Genetic Algorithms." Information Processing & Management 51, no. 3 (May 2015): 306–28. http://dx.doi.org/10.1016/j.ipm.2014.12.002.

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Dominguez, Raul, Mark Post, Alexander Fabisch, Romain Michalec, Vincent Bissonnette, and Shashank Govindaraj. "Common Data Fusion Framework: An open-source Common Data Fusion Framework for space robotics." International Journal of Advanced Robotic Systems 17, no. 2 (March 1, 2020): 172988142091176. http://dx.doi.org/10.1177/1729881420911767.

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Multisensor data fusion plays a vital role in providing autonomous systems with environmental information crucial for reliable functioning. In this article, we summarize the modular structure of the newly developed and released Common Data Fusion Framework and explain how it is used. Sensor data are registered and fused within the Common Data Fusion Framework to produce comprehensive 3D environment representations and pose estimations. The proposed software components to model this process in a reusable manner are presented through a complete overview of the framework, then the provided data fusion algorithms are listed, and through the case of 3D reconstruction from 2D images, the Common Data Fusion Framework approach is exemplified. The Common Data Fusion Framework has been deployed and tested in various scenarios that include robots performing operations of planetary rover exploration and tracking of orbiting satellites.

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Guan, Hongcan, Yanjun Su, Tianyu Hu, Jin Chen, and Qinghua Guo. "An Object-Based Strategy for Improving the Accuracy of Spatiotemporal Satellite Imagery Fusion for Vegetation-Mapping Applications." Remote Sensing 11, no. 24 (December 6, 2019): 2927. http://dx.doi.org/10.3390/rs11242927.

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Spatiotemporal data fusion is a key technique for generating unified time-series images from various satellite platforms to support the mapping and monitoring of vegetation. However, the high similarity in the reflectance spectrum of different vegetation types brings an enormous challenge in the similar pixel selection procedure of spatiotemporal data fusion, which may lead to considerable uncertainties in the fusion. Here, we propose an object-based spatiotemporal data-fusion framework to replace the original similar pixel selection procedure with an object-restricted method to address this issue. The proposed framework can be applied to any spatiotemporal data-fusion algorithm based on similar pixels. In this study, we modified the spatial and temporal adaptive reflectance fusion model (STARFM), the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the flexible spatiotemporal data-fusion model (FSDAF) using the proposed framework, and evaluated their performances in fusing Sentinel 2 and Landsat 8 images, Landsat 8 and Moderate-resolution Imaging Spectroradiometer (MODIS) images, and Sentinel 2 and MODIS images in a study site covered by grasslands, croplands, coniferous forests, and broadleaf forests. The results show that the proposed object-based framework can improve all three data-fusion algorithms significantly by delineating vegetation boundaries more clearly, and the improvements on FSDAF is the greatest among all three algorithms, which has an average decrease of 2.8% in relative root-mean-square error (rRMSE) in all sensor combinations. Moreover, the improvement on fusing Sentinel 2 and Landsat 8 images is more significant (an average decrease of 2.5% in rRMSE). By using the fused images generated from the proposed object-based framework, we can improve the vegetation mapping result by significantly reducing the “pepper-salt” effect. We believe that the proposed object-based framework has great potential to be used in generating time-series high-resolution remote-sensing data for vegetation mapping applications.

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Wang, Jie Gui. "New Method of Moving Targets Passive Tracking by Single Moving Observer Based on Measurement Data Fusion." Applied Mechanics and Materials 239-240 (December 2012): 942–45. http://dx.doi.org/10.4028/www.scientific.net/amm.239-240.942.

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Moving targets passive tracking by single moving observer is a difficult problem. A new location method based on measurement data fusion is proposed in this paper. Firstly, the adaptive passive tracking initiation algorithm is introduced. Secondly, a new data association algorithm is proposed, based on the data fusion of multiple measurements, the decision of synthetic data association is made. Finally, with the help of computer simulations, the proposed algorithms are proven to be correct and effective.

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Wang, Tao, Xiaoran Wang, and Mingyu Hong. "Gas Leak Location Detection Based on Data Fusion with Time Difference of Arrival and Energy Decay Using an Ultrasonic Sensor Array." Sensors 18, no. 9 (September 7, 2018): 2985. http://dx.doi.org/10.3390/s18092985.

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Ultrasonic gas leak location technology is based on the detection of ultrasonic waves generated by the ejection of pressured gas from leak holes in sealed containers or pipes. To obtain more accurate leak location information and determine the locations of leak holes in three-dimensional space, this paper proposes an ultrasonic leak location approach based on multi-algorithm data fusion. With the help of a planar ultrasonic sensor array, the eigenvectors of two individual algorithms, i.e., the arrival distance difference, as determined from the time difference of arrival (TDOA) location algorithm, and the ratio of arrival distances from the energy decay (ED) location algorithm, are extracted and fused to calculate the three-dimensional coordinates of leak holes. The fusion is based on an extended Kalman filter, in which the results of the individual algorithms are seen as observation values. The final system state matrix is composed of distances between the measured leak hole and the sensors. Our experiments show that, under the condition in which the pressure in the measured container is 100 kPa, and the leak hole–sensor distance is 800 mm, the maximum error of the calculated results based on the data fusion location algorithm is less than 20 mm, and the combined accuracy is better than those of the individual location algorithms.

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Arunkumar, A., D. Surendran, and S. Sreya. "Data Fusion and Machine Learning in Medical Diagnosis: A Bird Eye View." Journal of Computational and Theoretical Nanoscience 16, no. 12 (December 1, 2019): 5127–33. http://dx.doi.org/10.1166/jctn.2019.8574.

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With the invent of computer-mediated technologies, urge of medical diagnosis, surveillance system and the rapid development in satellite and sensor networks, demands an efficient data fusion techniques, methodologies and machine learning algorithms. Expert system and Data fusion has materialized as a promising research area for medical diagnosis in the upcoming years. In Data fusion, information may be in various nature: it ranges from measurements to verbal reports. Data fusion is a framework for analysis of data sets such that different datasets can interact and inform each other. Machine learning together with data fusion provides results with high accuracy and prediction. This paper presents a comparative analysis of existing expert systems for medical diagnosis which uses data fusion and machine learning algorithms to diagnose various diseases.

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Ren, Jintong, Wunian Yang, Xin Yang, Xiaoyu Deng, He Zhao, Fang Wang, and Lei Wang. "Optimization of Fusion Method for GF-2 Satellite Remote Sensing Images based on the Classification Effect." Earth Sciences Research Journal 23, no. 2 (April 1, 2019): 163–69. http://dx.doi.org/10.15446/esrj.v23n2.80281.

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With the successful launch of China’s GF series satellites, it is more important to study the image data quality, the adaptability of processing method and information extraction method. The panchromatic and multi-spectral data which is based on the GF-2 images data of Chinese sub-meter high-resolution remote sensing satellite is fused by PCA, Pansharp, Gram-Schmidt and NNDiffuse fusion. Then, the quality of the fusion images were evaluated subjectively and objectively. In order to evaluate the applicability of different classification algorithms to the classification, the object-oriented classification algorithm which is based on machine learning algorithm, such as KNN, SVM and Random Trees were used to classify the different GF-2 fusion images. The results showed that: (1) The best visual effect of GF-2 fusion image was the Pansharp fusion image; The quantitative evaluation results showed that the brightness and information retention of Gram-Schmidt fusion image was the best，while the Pansharp fusion image had the highest correlation with the original multi-spectral image; the NNDiffuse fusion image had the highest clarity, and the PCA fusion image quantitative evaluation effect was the worst; (2) According to the applicability analysis of the fusion images based on different classification algorithms with features information extraction, it could be seen that the NNDiffuse fusion method was used for the fusion of GF-2 image data, and the classification of the fusion images was more suitable by using KNN or Random Trees classification algorithm.

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Yan, Hongru, Huaqi Chai, and Yang Dai. "A management of early warning and risk control based on data fusion for COVID-19." Journal of Intelligent & Fuzzy Systems 39, no. 6 (December 4, 2020): 8989–96. http://dx.doi.org/10.3233/jifs-189297.

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According to the previous management of early warning and risk control methods, the efficiency of management prediction is low, the effect is not good, and the disadvantages are very obvious. This paper mainly studies the C4.5 algorithm, Apriori algorithm and K-means algorithm. On the basis of association rules, the data from the above three algorithms are fused. On the fusion results of the processed data, it builds and optimizes the early warning model. The fusion data used in this model can be regarded as the basic data and the association rules are used for data mining. The experimental results show that data fusion can solve the problems of management early warning and risk control. This method is applied to enterprises Management has reference value.

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Qian, Yang, Zhongtian Yang, and Haowei Zeng. "Direct Position Determination for Augmented Coprime Arrays via Weighted Subspace Data Fusion Method." Mathematical Problems in Engineering 2021 (July 15, 2021): 1–10. http://dx.doi.org/10.1155/2021/2825025.

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Direct position determination (DPD) for augmented coprime arrays is investigated in this paper. Augmented coprime array expands degree of freedom and array aperture and improves positioning accuracy. Because of poor stability and noise sensitivity of the subspace data fusion (SDF) method, we propose two weighted subspace data fusion (W-SDF) algorithms for direct position determination. Simulation results show that two W-SDF algorithms have a prominent promotion in positioning accuracy than SDF, Capon, and propagator method (PM) algorithm for augmented coprime arrays. SDF based on optimal weighting (OW-SDF) is slightly better than SDF based on SNR weighting (SW-SDF) in positioning accuracy. The performance for DPD of the W-SDF method with augmented coprime arrays is better than that of the W-SDF method with uniform arrays.

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Sun, Guiling, Ziyang Zhang, Bowen Zheng, and Yangyang Li. "Multi-Sensor Data Fusion Algorithm Based on Trust Degree and Improved Genetics." Sensors 19, no. 9 (May 8, 2019): 2139. http://dx.doi.org/10.3390/s19092139.

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Aiming at the problems of low data fusion precision and poor stability in greenhouse wireless sensor networks (WSNs), a multi-sensor data fusion algorithm based on trust degree and improved genetics is proposed. The original data collected by the sensor nodes are sent to the gateway through the sink node, and data preprocessing based on cubic exponential smoothing is performed at the gateway to eliminate abnormal data and noise data. In fuzzy theory, the range of membership functions is determined, according to this feature, the data fusion algorithm based on exponential trust degree is used to fuse the smooth data to avoid the absolute degree of mutual trust between data. In this paper, we have improved the crossover and mutation operations in the standard genetic algorithm, the variation is separated from the intersection, the chaotic sequence is used to determine the intersection, and the weakest single-point intersection is implemented to improve the convergence accuracy of the algorithm, weaken and avoid jitter problems during optimization. The chaotic sequence is used to mutate multiple genes in the chromosome to avoid premature algorithm maturity. Finally, the improved genetic algorithm is used to optimize the fusion estimation value. The experimental results show that the cubic exponential smoothing can significantly reduce the data fluctuation and improve the stability of the system. Compared with the commonly used data fusion algorithms such as arithmetic average method and adaptive weighting method, the data fusion algorithm based on trust degree and improved genetics has higher fusion precision. At the same time, the execution time of the algorithm is greatly reduced.

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Ottaviano, Flavia, Fabing Cui, and Andy H. F. Chow. "Modeling and Data Fusion of Dynamic Highway Traffic." Transportation Research Record: Journal of the Transportation Research Board 2644, no. 1 (January 2017): 92–99. http://dx.doi.org/10.3141/2644-11.

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This paper presents a data fusion framework for processing and integrating data collected from heterogeneous sources on motorways to generate short-term predictions. Considering the heterogeneity in spatiotemporal granularity in data from different sources, an adaptive kernel-based smoothing method was first used to project all data onto a common space–time grid. The data were then integrated through a Kalman filter framework build based on the cell transmission model for generating short-term traffic state prediction. The algorithms were applied and tested with real traffic data collected from the California I-880 corridor in the San Francisco Bay Area from the Mobile Century experiment. Results revealed that the proposed fusion algorithm can work with data sources that are different in their spatiotemporal granularity and improve the accuracy of state estimation through incorporating multiple data sources. The present work contributed to the field of traffic engineering and management with the application of big data analytics.

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Cao, Jian, and Cong Yan. "Research on Information Fusion of Two New DLL Discriminator Algorithms." Applied Mechanics and Materials 543-547 (March 2014): 1223–26. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.1223.

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After information fusion model has been established, the feature-level fusion algorithm based on fuzzy neural network and expert system is proposed, in which the expert system has been embedded into fuzzy neural network so that it could choose the membership function and adjust the network structure. At the same time, for code tracking loop, two new code phase discriminator algorithms based on DLL structure is proposed. Evidence theory has been applied to achieve the decision-making level fusion. The performances of the two algorithms were studied by using theoretical method and experimental method with analog IF signal data and actual IF signal data respectively. Then, the results of feature-level fusion have been taken as the evidences to construct the frame of discernment. The research results show that the process of information fusion has abilities of adapting and self-learning.

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Liu, Qiuxia. "Intelligent Water Quality Monitoring System Based on Multi-Sensor Data Fusion Technology." International Journal of Ambient Computing and Intelligence 12, no. 4 (October 2021): 43–63. http://dx.doi.org/10.4018/ijaci.2021100103.

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The intelligent water quality monitoring system takes the single chip microcomputer STM32F103C8T6 as the control core to collect signals of each sensor module and converts the collected parameters into effective digital signals by using the internal analog-to-digital converter. The data gathered by the acquisition center is sent to the analysis and processing center through the ZigBee module E18. In the analysis and processing center, data is fused and processed by the single chip microcomputer STC12C5A60S2. The data after fusion is sent to the monitoring management center through the GPRS module SIM800C. For improving the monitoring precision of the system, multi-level data fusion algorithms are used. In the data layer, abnormal values are deleted by abnormal data detection method, and the median average filtering method is used to fuse the data; the algorithm based on weighted estimation fusion is used in the feature layer; the fuzzy control fusion algorithm is used in the decision.

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Wu, Zhong, and Chuan Zhou. "Construction of an Intelligent Processing Platform for Equestrian Event Information Based on Data Fusion and Data Mining." Journal of Sensors 2021 (July 23, 2021): 1–9. http://dx.doi.org/10.1155/2021/1869281.

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In the past two years, equestrian sports have become more and more popular with the public. Due to the comprehensive development of equestrian preparations for the 2020 Olympic Games in China, the equestrian sports industry presents an unprecedented favorable development environment in China. This article is aimed at studying the construction of an equestrian event information intelligent processing platform based on data fusion and data mining. This article introduces the relevant theoretical knowledge of data mining and data fusion, including the description of the concept of data mining, the common analysis methods and algorithms of data mining, the basic concepts of data fusion, and the functional structure of data fusion. It discusses various algorithms in cluster analysis and focuses on the analysis of distance measurement and similarity coefficient in cluster analysis. In the experimental part, in order to intelligently process and acquire information, an information intelligent processing platform is constructed based on data fusion and data mining technology. The experimental results of this paper show that the precision rate, recall rate, and F -score of the platform under closed test are much higher than those under open test, and the precision rate is increased by about 7.26%.

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SHENBAGAVADIVU, S., KUMAR M. SENTHIL, and RAJAN B. CHIDHAMBARA. "SIGNIFICANCE OF DATA FUSION ALGORITHMS IN IOT ENVIRONMENT-A REVIEW." i-manager's Journal on Information Technology 8, no. 3 (2019): 42. http://dx.doi.org/10.26634/jit.8.3.16734.

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Ankarao, V., V. Sowmya, and K. P. Soman. "Multi-sensor data fusion using NIHS transform and decomposition algorithms." Multimedia Tools and Applications 77, no. 23 (May 24, 2018): 30381–402. http://dx.doi.org/10.1007/s11042-018-6114-2.

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Perdikaris, P., M. Raissi, A. Damianou, N. D. Lawrence, and G. E. Karniadakis. "Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 473, no. 2198 (February 2017): 20160751. http://dx.doi.org/10.1098/rspa.2016.0751.

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Multi-fidelity modelling enables accurate inference of quantities of interest by synergistically combining realizations of low-cost/low-fidelity models with a small set of high-fidelity observations. This is particularly effective when the low- and high-fidelity models exhibit strong correlations, and can lead to significant computational gains over approaches that solely rely on high-fidelity models. However, in many cases of practical interest, low-fidelity models can only be well correlated to their high-fidelity counterparts for a specific range of input parameters, and potentially return wrong trends and erroneous predictions if probed outside of their validity regime. Here we put forth a probabilistic framework based on Gaussian process regression and nonlinear autoregressive schemes that is capable of learning complex nonlinear and space-dependent cross-correlations between models of variable fidelity, and can effectively safeguard against low-fidelity models that provide wrong trends. This introduces a new class of multi-fidelity information fusion algorithms that provide a fundamental extension to the existing linear autoregressive methodologies, while still maintaining the same algorithmic complexity and overall computational cost. The performance of the proposed methods is tested in several benchmark problems involving both synthetic and real multi-fidelity datasets from computational fluid dynamics simulations.

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You, He, and Zhang Jingwei. "New track correlation algorithms in a multisensor data fusion system." IEEE Transactions on Aerospace and Electronic Systems 42, no. 4 (October 2006): 1359–71. http://dx.doi.org/10.1109/taes.2006.314577.

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Guo, Yangnan, Cangjiao Wang, Shaogang Lei, Junzhe Yang, and Yibo Zhao. "A Framework of Spatio-Temporal Fusion Algorithm Selection for Landsat NDVI Time Series Construction." ISPRS International Journal of Geo-Information 9, no. 11 (November 4, 2020): 665. http://dx.doi.org/10.3390/ijgi9110665.

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Spatio-temporal fusion algorithms dramatically enhance the application of the Landsat time series. However, each spatio-temporal fusion algorithm has its pros and cons of heterogeneous land cover performance, the minimal number of input image pairs, and its efficiency. This study aimed to answer: (1) how to determine the adaptability of the spatio-temporal fusion algorithm for predicting images in prediction date and (2) whether the Landsat normalized difference vegetation index (NDVI) time series would benefit from the interpolation with images fused from multiple spatio-temporal fusion algorithms. Thus, we supposed a linear relationship existed between the fusion accuracy and spatial and temporal variance. Taking the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and the Enhanced STARFM (ESTARFM) as basic algorithms, a framework was designed to screen a spatio-temporal fusion algorithm for the Landsat NDVI time series construction. The screening rule was designed by fitting the linear relationship between the spatial and temporal variance and fusion algorithm accuracy, and then the fitted relationship was combined with the graded accuracy selecting rule (R2) to select the fusion algorithm. The results indicated that the constructed Landsat NDVI time series by this paper proposed framework exhibited the highest overall accuracy (88.18%), and lowest omission (1.82%) and commission errors (10.00%) in land cover change detection compared with the moderate resolution imaging spectroradiometer (MODIS) NDVI time series and the NDVI time series constructed by a single STARFM or ESTARFM. Phenological stability analysis demonstrated that the Landsat NDVI time series established by multiple spatio-temporal algorithms could effectively avoid phenological fluctuations in the time series constructed by a single fusion algorithm. We believe that this framework can help improve the quality of the Landsat NDVI time series and fulfill the gap between near real-time environmental monitoring mandates and data-scarcity reality.

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Büscher, Nils, Daniel Gis, Volker Kühn, and Christian Haubelt. "On the Functional and Extra-Functional Properties of IMU Fusion Algorithms for Body-Worn Smart Sensors." Sensors 21, no. 8 (April 13, 2021): 2747. http://dx.doi.org/10.3390/s21082747.

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In this work, four sensor fusion algorithms for inertial measurement unit data to determine the orientation of a device are assessed regarding their usability in a hardware restricted environment such as body-worn sensor nodes. The assessment is done for both the functional and the extra-functional properties in the context of human operated devices. The four algorithms are implemented in three data formats: 32-bit floating-point, 32-bit fixed-point and 16-bit fixed-point and compared regarding code size, computational effort, and fusion quality. Code size and computational effort are evaluated on an ARM Cortex M0+. For the assessment of the functional properties, the sensor fusion output is compared to a camera generated reference and analyzed in an extensive statistical analysis to determine how data format, algorithm, and human interaction influence the quality of the sensor fusion. Our experiments show that using fixed-point arithmetic can significantly decrease the computational complexity while still maintaining a high fusion quality and all four algorithms are applicable for applications with human interaction.

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Xue, Jie, Yee Leung, and Tung Fung. "An Unmixing-Based Bayesian Model for Spatio-Temporal Satellite Image Fusion in Heterogeneous Landscapes." Remote Sensing 11, no. 3 (February 6, 2019): 324. http://dx.doi.org/10.3390/rs11030324.

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Studies of land surface dynamics in heterogeneous landscapes often require satellite images with a high resolution, both in time and space. However, the design of satellite sensors often inherently limits the availability of such images. Images with high spatial resolution tend to have relatively low temporal resolution, and vice versa. Therefore, fusion of the two types of images provides a useful way to generate data high in both spatial and temporal resolutions. A Bayesian data fusion framework can produce the target high-resolution image based on a rigorous statistical foundation. However, existing Bayesian data fusion algorithms, such as STBDF (spatio-temporal Bayesian data fusion) -I and -II, do not fully incorporate the mixed information contained in low-spatial-resolution pixels, which in turn might limit their fusion ability in heterogeneous landscapes. To enhance the capability of existing STBDF models in handling heterogeneous areas, this study proposes two improved Bayesian data fusion approaches, coined ISTBDF-I and ISTBDF-II, which incorporate an unmixing-based algorithm into the existing STBDF framework. The performance of the proposed algorithms is visually and quantitatively compared with STBDF-II using simulated data and real satellite images. Experimental results show that the proposed algorithms generate improved spatio-temporal-resolution images over STBDF-II, especially in heterogeneous areas. They shed light on the way to further enhance our fusion capability.

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Qi, Jin, Fei Jiang, Xiaojun Wang, Bin Xu, and Yanfei Sun. "Community Clustering Algorithm in Complex Networks Based on Microcommunity Fusion." Mathematical Problems in Engineering 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/754029.

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With the further research on physical meaning and digital features of the community structure in complex networks in recent years, the improvement of effectiveness and efficiency of the community mining algorithms in complex networks has become an important subject in this area. This paper puts forward a concept of the microcommunity and gets final mining results of communities through fusing different microcommunities. This paper starts with the basic definition of the network community and appliesExpansionto the microcommunity clustering which provides prerequisites for the microcommunity fusion. The proposed algorithm is more efficient andhas higher solution qualitycompared with other similar algorithms through the analysis of test results based on network data set.

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Hazaymeh, K., and A. Almagbile. "A COMPARATIVE ANALYSIS OF SPATIOTEMPORAL DATA FUSION MODELS FOR LANDSAT AND MODIS DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 491–95. http://dx.doi.org/10.5194/isprs-archives-xlii-3-491-2018.

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In this study, three documented spatiotemporal data fusion models were applied to Landsat-7 and MODIS surface reflectance, and NDVI. The algorithms included the spatial and temporal adaptive reflectance fusion model (STARFM), sparse representation based on a spatiotemporal reflectance fusion model (SPSTFM), and spatiotemporal image-fusion model (STI-FM). The objectives of this study were to (i) compare the performance of these three fusion models using a one Landsat-MODIS spectral reflectance image pairs using time-series datasets from the Coleambally irrigation area in Australia, and (ii) quantitatively evaluate the accuracy of the synthetic images generated from each fusion model using statistical measurements. Results showed that the three fusion models predicted the synthetic Landsat-7 image with adequate agreements. The STI-FM produced more accurate reconstructions of both Landsat-7 spectral bands and NDVI. Furthermore, it produced surface reflectance images having the highest correlation with the actual Landsat-7 images. This study indicated that STI-FM would be more suitable for spatiotemporal data fusion applications such as vegetation monitoring, drought monitoring, and evapotranspiration.

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Klečka, Jan, Karel Horák, and Ondřej Boštík. "General concepts of multi-sensor data-fusion based SLAM." IAES International Journal of Robotics and Automation (IJRA) 9, no. 2 (June 1, 2020): 63. http://dx.doi.org/10.11591/ijra.v9i2.pp63-72.

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<p>This paper is approaching a problem of Simultaneous Localization and Mapping (SLAM) algorithms focused specifically on processing of data from a heterogeneous set of sensors concurrently. Sensors are considered to be different in a sense of measured physical quantity and so the problem of effective data-fusion is discussed. A special extension of the standard probabilistic approach to SLAM algorithms is presented. This extension is composed of two parts. Firstly is presented general perspective multiple-sensors based SLAM and then thee archetypical special cases are discuses. One archetype provisionally designated as "partially collective mapping" has been analyzed also in a practical perspective because it implies a promising options for implicit map-level data-fusion.</p>

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Zhen, Chen. "Using Big Data Fuzzy K-Means Clustering and Information Fusion Algorithm in English Teaching Ability Evaluation." Complexity 2021 (February 5, 2021): 1–9. http://dx.doi.org/10.1155/2021/5554444.

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Aiming at the problem of inaccurate classification of big data information in traditional English teaching ability evaluation algorithms, an English teaching ability evaluation algorithm based on big data fuzzy K-means clustering and information fusion is proposed. Firstly, the author uses the idea of K-means clustering to analyze the collected original error data, such as teacher level, teaching facility investment, and policy relevance level, removes the data that the algorithm considers unreliable, uses the remaining valid data to calculate the weighting factor of the modified fuzzy logic algorithm, and evaluates the weighted average with the node measurement data and gets the final fusion value. Secondly, the author integrates the big data information fusion and K-means clustering algorithm, realizes the clustering and integration of the index parameters of English teaching ability, compiles the corresponding English teaching resource allocation plan, and realizes the evaluation of English teaching ability. Finally, the results show that using this method to evaluate English teaching ability has better information fusion analysis ability, which improves the accuracy of teaching ability evaluation and the efficiency of teaching resources application.

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Zlatev, Zlatko, Galina Veres, and Zoheir Sabeur. "Agile Data Fusion and Knowledge Base Architecture for Critical Decision Support." International Journal of Decision Support System Technology 5, no. 2 (April 2013): 1–20. http://dx.doi.org/10.4018/jdsst.2013040101.

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This paper describes the architecture and deployment of a software platform for information fusion, knowledge hosting and critical decision support. The work has been carried out under the TRIDEC project (www.tridec-online.eu), focusing on geo-information fusion and collaborative decision making. Four technologies underpin the architecture: 1) A message oriented middleware, for distributed communications; 2) A leveraged hybrid storage solution, for efficient storage of heterogeneous datasets and semantic knowledge; 3) A generic data fusion container, for dynamic algorithms control; and 4) A single conceptual model and schema, as systems’ semantic meta-model. Deployment for industrial drilling operations is described. Agility is manifested with the ability to integrate data sources from a proprietary domain, dynamically discover new datasets and configure and task fusion algorithms to operate on them, aided by efficient information storage. The platform empowers decision support by enabling dynamic discovery of information and control of the fusion process across geo-distributed locations.

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Journal articles on the topic 'Data fusion algorithms'

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