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Journal articles on the topic 'Data fusion algorithms'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

ZHU, SHANFENG, QIZHI FANG, and WEIMIN ZHENG. "SOCIAL CHOICE FOR DATA FUSION." International Journal of Information Technology & Decision Making 03, no. 04 (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 w

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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 (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 (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 th

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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 (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 var

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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 evalua

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Wu, Jian, Liang Xu, Qi Chen, and Zhihui Ye. "Multi-sensor data fusion path combining fuzzy theory and neural networks." Intelligent Decision Technologies 18, no. 4 (2024): 3365–78. https://doi.org/10.3233/idt-240316.

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In the development of automation and intelligent systems, multi-sensor data fusion technology is crucial. However, due to the uncertainty and incompleteness of sensor data, how to effectively fuse these data has always been a challenge. To solve this problem, the study combines fuzzy theory and neural networks to study the process of multi-sensor data transmission and data fusion. Sensor network clustering algorithms based on whale algorithm optimized fuzzy logic and neural network data fusion algorithms based on sparrow algorithm optimized were designed respectively. The performance test resu

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Cui, Haiting, and Shanshan Li. "Controllable Clustering Algorithm for Associated Real-Time Streaming Big Data Based on Multi-Source Data Fusion." Wireless Communications and Mobile Computing 2022 (February 23, 2022): 1–9. http://dx.doi.org/10.1155/2022/5244695.

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Aiming at the problems of poor security and clustering accuracy in current data clustering algorithms, a controllable clustering algorithm for real-time streaming big data based on multi-source data fusion is proposed. The FIR filter structure model is used to suppress network interference, and ant colony algorithm is used to detect the abnormal data in the big data. By optimizing the iteration, the pheromone concentration is placed in the front position as the abnormal data point, and the filter is introduced. The fusion scope of multi-source data fusion is set. Combined with the data similar

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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

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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 (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-

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Lv, Lihua. "RFID Data Analysis and Evaluation Based on Big Data and Data Clustering." Computational Intelligence and Neuroscience 2022 (March 26, 2022): 1–10. http://dx.doi.org/10.1155/2022/3432688.

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The era people live in is the era of big data, and massive data carry a large amount of information. This study aims to analyze RFID data based on big data and clustering algorithms. In this study, a RFID data extraction technology based on joint Kalman filter fusion is proposed. In the system, the proposed data extraction technology can effectively read RFID tags. The data are recorded, and the KM-KL clustering algorithm is proposed for RFID data, which combines the advantages of the K-means algorithm. The improved KM-KL clustering algorithm can effectively analyze and evaluate RFID data. The

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Ruipeng, Tang, Yang Jianbu, Tang Jianrui, Narendra Kumar Aridas, and Mohamad Sofian Abu Talip. "Design of agricultural wireless sensor network node optimization method based on improved data fusion algorithm." PLOS ONE 19, no. 11 (2024): e0308845. http://dx.doi.org/10.1371/journal.pone.0308845.

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The agricultural WSN (wireless sensor network) has the characteristics of long operation cycle and wide coverage area. In order to cover as much area as possible, farms usually deploy multiple monitoring devices in different locations of the same area. Due to different types of equipment, monitoring data will vary greatly, and too many monitoring nodes also reduce the efficiency of the network. Although there have been some studies on data fusion algorithms, they have problems such as ignoring the dynamic changes of time series, weak anti-interference ability, and poor processing of data fluct

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Jiang, Lan. "Artificial Intelligence Algorithms for Multisensor Information Fusion Based on Deep Learning Algorithms." Mobile Information Systems 2022 (April 13, 2022): 1–10. http://dx.doi.org/10.1155/2022/3356213.

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Artificial intelligence (AI) has been widely used all over the world. AI can be applied not only in mechanical learning and expert system but also in knowledge engineering and intelligent information retrieval and has achieved amazing results. This article aims to study the relevant knowledge of deep learning algorithms and multisensor information fusion and how to use deep learning algorithms and multisensor information fusion to study AI algorithms. This paper raises the question of whether the improved multisensor information fusion will affect the AI algorithm. From the data in the experim

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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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Jiang, Mengmeng, Qiong Wu, and Xuetao Li. "Multisource Heterogeneous Data Fusion Analysis of Regional Digital Construction Based on Machine Learning." Journal of Sensors 2022 (January 10, 2022): 1–11. http://dx.doi.org/10.1155/2022/8205929.

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In modern urban construction, digitalization has become a trend, but the single source of information of traditional algorithms can not meet people’s needs, so the data fusion technology needs to draw estimation and judgment from multisource data to increase the confidence of data, improve reliability, and reduce uncertainty. In order to understand the influencing factors of regional digitalization, this paper conducts multisource heterogeneous data fusion analysis based on regional digitalization of machine learning, using decision tree and artificial neural network algorithm, compares the ma

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Liu, Binglin, Qian Li, Zhihua Zheng, et al. "A Review of Multi-Source Data Fusion and Analysis Algorithms in Smart City Construction: Facilitating Real Estate Management and Urban Optimization." Algorithms 18, no. 1 (2025): 30. https://doi.org/10.3390/a18010030.

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In the context of the booming construction of smart cities, multi-source data fusion and analysis algorithms play a key role in optimizing real estate management and improving urban efficiency. In this review, we comprehensively and systematically review the relevant algorithms, covering the types, characteristics, fusion techniques, analysis algorithms, and their synergies of multi-source data. We found that multi-source data, including sensors, social media, citizen feedback, and GIS data, face challenges such as data quality and privacy security when being fused. Data fusion algorithms are

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Jiang, Chen, Dongbao Zhao, Qiuzhao Zhang, and Wenkai Liu. "A Multi-GNSS/IMU Data Fusion Algorithm Based on the Mixed Norms for Land Vehicle Applications." Remote Sensing 15, no. 9 (2023): 2439. http://dx.doi.org/10.3390/rs15092439.

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As a typical application of geodesy, the GNSS/INS (Global Navigation Satellite System and Inertial Navigation System) integrated navigation technique was developed and has been applied for decades. For the integrated systems with multiple sensors, data fusion is one of the key problems. As a well-known data fusion algorithm, the Kalman filter can provide optimal estimates with known parameters of the models and noises. In the literature, however, the data fusion algorithm of the GNSS/INS integrated navigation and positioning systems is performed under a certain norm, and performance of the con

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Wang, Cui. "Advanced Intelligent English Translation Based on Multisensor Data Fusion Optimization." Journal of Sensors 2022 (September 12, 2022): 1–10. http://dx.doi.org/10.1155/2022/5951127.

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English translation activity course is of great significance to cultivate students’ English translation level. In the context of multisensor data fusion, how to effectively carry out English translation activity course in colleges and universities has become an important topic. The educational value and intellectual property of advanced Intelligent English translation activity course are analyzed. From multisensor data fusion and the improvement of translation, translators psychological changes of the boot and prominent features, English translation activity and translation, the generality of

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Li, Cheng Yang. "Research and comparison of algorithms based on multi-modal fusion." Journal of Physics: Conference Series 2807, no. 1 (2024): 012038. http://dx.doi.org/10.1088/1742-6596/2807/1/012038.

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Abstract A multi-modal fusion algorithm is an important method for information fusion on multi-modal data provided by different sensors. It can make full use of the advantages of multiple sensors and improve the accuracy and robustness of data processing and decision-making. This paper aims to study the performance difference between the extended Kalman filter (EKF) algorithm and other algorithms in multi-modal fusion and explore a method to fuse multiple algorithms further to improve the accuracy of fusion results. The author uses the classic test set data set for experiments to evaluate the

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Chen, Guo, Zhigui Liu, Guang Yu, and Jianhong Liang. "A New View of Multisensor Data Fusion: Research on Generalized Fusion." Mathematical Problems in Engineering 2021 (October 15, 2021): 1–21. http://dx.doi.org/10.1155/2021/5471242.

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Multisensor data generalized fusion algorithm is a kind of symbolic computing model with multiple application objects based on sensor generalized integration. It is the theoretical basis of numerical fusion. This paper aims to comprehensively review the generalized fusion algorithms of multisensor data. Firstly, the development and definition of multisensor data fusion are analyzed and the definition of multisensor data generalized fusion is given. Secondly, the classification of multisensor data fusion is discussed, and the generalized integration structure of multisensor and its data acquisi

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Araujo-Neto, Wolmar, Leonardo Rocha Olivi, Daniel Khede Dourado Villa, and Mário Sarcinelli-Filho. "Data Fusion Applied to the Leader-Based Bat Algorithm to Improve the Localization of Mobile Robots." Sensors 25, no. 2 (2025): 403. https://doi.org/10.3390/s25020403.

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The increasing demand for autonomous mobile robots in complex environments calls for efficient path-planning algorithms. Bio-inspired algorithms effectively address intricate optimization challenges, but their computational cost increases with the number of particles, which is great when implementing algorithms of high accuracy. To address such topics, this paper explores the application of the leader-based bat algorithm (LBBA), an enhancement of the traditional bat algorithm (BA). By dynamically incorporating robot orientation as a guiding factor in swarm distribution, LBBA improves mobile ro

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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 (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 trainin

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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 interdepen

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Wang, Jimin, Mingwei Jiang, Changzheng Ji, and Lei Zhang. "Penetration Depth Prediction Based on Data Fusion." Journal of Physics: Conference Series 2203, no. 1 (2022): 012076. http://dx.doi.org/10.1088/1742-6596/2203/1/012076.

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Abstract This paper presents a penetration depth prediction model based on data fusion. The parameters of the penetration analysis are divided into different evaluation spaces, and then empirical algorithms are evaluated and the better algorithm is selected in each evaluation space. A large number of simulation data is generated to solve the problem of lack of experimental data. Two BP neural network prediction models are built based on experiment data and simulation data, respectively, and the genetic algorithm is used for parameter optimization. Finally, the attention mechanism is used to fu

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Wang, Yuechun, Suzhen Zhang, and Shaofang Zhang. "Network Data Mining Algorithm of Associated Users Based on Multi-Information Fusion." Security and Communication Networks 2022 (July 15, 2022): 1–7. http://dx.doi.org/10.1155/2022/9656986.

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To explore how related users can optimize the network mining algorithm, the author proposes a related user mining algorithm based on the fusion of user attributes and user relationships. This method recommends key technical problems and solutions based on information represented by multi-information fusion and explores research on associated user network data mining algorithms. Research has shown that the associated user network data mining algorithm based on multi-information fusion is 65% higher than previous methods. AUMA-MRL has good performance under different network overlaps. Also, sinc

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Fan, Li. "Research on Autonomous Obstacle Avoidance Algorithm for Complex Environment of Unmanned Aerial Vehicle Based on Multi-source Sensor Fusion." MATEC Web of Conferences 410 (2025): 04008. https://doi.org/10.1051/matecconf/202541004008.

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Autonomous obstacle avoidance for UAVs in complex environments is crucial, single sensors have limitations, and multi-source sensor fusion technology has received attention. Based on the above problems, this paper summarizes the research on autonomous obstacle avoidance algorithms for UAVs in complex environments based on multi- source sensor fusion in recent years. Firstly, the classification and basic principles of multi-source sensor fusion algorithms at the data layer, feature layer and decision layer are sorted out, and the characteristics of commonly used sensors such as LiDAR and vision

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Villalpando-Hernandez, Rafaela, Cesar Vargas-Rosales, and David Munoz-Rodriguez. "Localization Algorithm for 3D Sensor Networks: A Recursive Data Fusion Approach." Sensors 21, no. 22 (2021): 7626. http://dx.doi.org/10.3390/s21227626.

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Location-based applications for security and assisted living, such as human location tracking, pet tracking and others, have increased considerably in the last few years, enabled by the fast growth of sensor networks. Sensor location information is essential for several network protocols and applications such as routing and energy harvesting, among others. Therefore, there is a need for developing new alternative localization algorithms suitable for rough, changing environments. In this paper, we formulate the Recursive Localization (RL) algorithm, based on the recursive coordinate data fusion

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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 const

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Kenyeres, Martin, Jozef Kenyeres, and Sepideh Hassankhani Dolatabadi. "Distributed Consensus Gossip-Based Data Fusion for Suppressing Incorrect Sensor Readings in Wireless Sensor Networks." Journal of Low Power Electronics and Applications 15, no. 1 (2025): 6. https://doi.org/10.3390/jlpea15010006.

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Incorrect sensor readings can cause serious problems in Wireless Sensor Networks (WSNs), potentially disrupting the operation of the entire system. As shown in the literature, they can arise from various reasons; therefore, addressing this issue has been a significant challenge for the scientific community over the past few decades. In this paper, we examine the applicability of seven distributed consensus gossip-based algorithms for sensor fusion (namely, the Randomized Gossip algorithm, the Geographic Gossip algorithm, three initial configurations of the Broadcast Gossip algorithm, the Push-

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Hassan, Emad S., Marwa Madkour, Salah E. Soliman, et al. "Energy-Efficient Data Fusion in WSNs Using Mobility-Aware Compression and Adaptive Clustering." Technologies 12, no. 12 (2024): 248. http://dx.doi.org/10.3390/technologies12120248.

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To facilitate energy-efficient information dissemination from multiple sensors to the sink within Wireless Sensor Networks (WSNs), in-network data fusion is imperative. This paper presents a new WSN topology that incorporates the Mobility-Efficient Data Fusion (MEDF) algorithm, which integrates a data-compression protocol with an adaptive-clustering mechanism. The primary goals of this topology are, first, to determine a dynamic sequence of cluster heads (CHs) for each data transmission round, aiming to prolong network lifetime by implementing an adaptive-clustering mechanism resilient to netw

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Zhou, Haiyang, Yixin Zhao, Yanzhong Liu, Sichao Lu, Xiang An, and Qiang Liu. "Multi-Sensor Data Fusion and CNN-LSTM Model for Human Activity Recognition System." Sensors 23, no. 10 (2023): 4750. http://dx.doi.org/10.3390/s23104750.

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Human activity recognition (HAR) is becoming increasingly important, especially with the growing number of elderly people living at home. However, most sensors, such as cameras, do not perform well in low-light environments. To address this issue, we designed a HAR system that combines a camera and a millimeter wave radar, taking advantage of each sensor and a fusion algorithm to distinguish between confusing human activities and to improve accuracy in low-light settings. To extract the spatial and temporal features contained in the multisensor fusion data, we designed an improved CNN-LSTM mod

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Carrara, Matteo, Marco Beccuti, Fulvio Lazzarato, et al. "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 encompass

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Wang, Hai, Junhao Liu, Haoran Dong, and Zheng Shao. "A Survey of the Multi-Sensor Fusion Object Detection Task in Autonomous Driving." Sensors 25, no. 9 (2025): 2794. https://doi.org/10.3390/s25092794.

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Multi-sensor fusion object detection is an advanced method that improves object recognition and tracking accuracy by integrating data from different types of sensors. As it can overcome the limitations of a single sensor in complex environments, the method has been widely applied in fields such as autonomous driving, intelligent monitoring, robot navigation, drone flight and so on. In the field of autonomous driving, multi-sensor fusion object detection has become a hot research topic. To further explore the future development trends of multi-sensor fusion object detection, we introduce the ma

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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 (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 extr

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

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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 (2008): 51–62. http://dx.doi.org/10.1049/iet-rsn:20060159.

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

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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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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 (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

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Amirah and Fitrah Karimah. "Leveraging Open Data with Machine Learning Algorithms." Jurnal Sistem Informasi dan Teknik Informatika (JAFOTIK) 1, no. 2 (2023): 62–69. https://doi.org/10.70356/jafotik.v1i2.19.

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In the evolving landscape of technology, the amalgamation of open data and machine learning stands as a powerful catalyst for innovation. This study explores the dynamic synergy between these domains, where open data's accessibility and transparency converge with machine learning's pattern recognition and predictive capabilities. The fusion holds immense promise across diverse sectors, from healthcare to finance, urban planning, and environmental science. By leveraging advanced algorithms on openly available information, organizations can gain unprecedented insights into trends, correlations,

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Cao, Zhitao. "Dynamic Allocation Method of Economic Information Integrated Data Based on Deep Learning Algorithm." Computational Intelligence and Neuroscience 2022 (May 16, 2022): 1–10. http://dx.doi.org/10.1155/2022/5494123.

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In view of the low efficiency of traditional data fusion algorithms in wireless sensor networks and the difficulty in processing high-dimensional data, a new algorithm CNNMDA, based on the deep learning model is proposed to realize data fusion. Firstly, the algorithm trains the constructed feature extraction model CNNM at the sink node; then each terminal node extracts the original data features through CNNM and finally sends the fused data to the sink node, so as to reduce the data transmission amount and prolong the network life. Simulation experiments show that compared with similar fusion

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Ren, Jintong, Wunian Yang, Xin Yang, et al. "Optimization of Fusion Method for GF-2 Satellite Remote Sensing Images based on the Classification Effect." Earth Sciences Research Journal 23, no. 2 (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 clas

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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 (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 algori

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Jiang, Chen, Qiuzhao Zhang, and Dongbao Zhao. "A New Data Fusion Method for GNSS/INS Integration Based on Weighted Multiple Criteria." Remote Sensing 16, no. 17 (2024): 3275. http://dx.doi.org/10.3390/rs16173275.

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The standard Kalman filter and most of its enhancements are typically designed based on the criterion that minimizes the mean squared error, with little discussion of multiple criteria in the positioning and navigation fields. Therefore, a novel data fusion method that takes into account weighted multiple criteria is proposed in this paper, implementing a filtering algorithm based on integrated criteria with different weights determined by a weight adjustment factor. The proposed algorithm and conventional filtering algorithms were utilized for data fusion in GNSS/INS integration. Experiments

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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

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Zhang, Xi, Jiyue Wang, Ying Huang, and Feiyue Zhu. "A novel industrial big data fusion method based on Q-learning and cascade classifier." Computer Science and Information Systems, no. 00 (2024): 51. http://dx.doi.org/10.2298/csis240314051z.

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The traditional industrial big data fusion algorithm has low efficiency and difficulty in processing high-dimensional data, this paper proposes a Q-learning-based cascade classifier model for industrial big data fusion. By combining cascade classifier and softmax classifier, feature extraction and data attribute classification of source industrial big data are completed in this cluster. In order to improve the classification rate, an improved Q-learning algorithm is proposed, which makes the improved algorithm randomly select actions in the early stage, and dynamically change in the late stage

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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 (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 le

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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 (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 t

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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 (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 re

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