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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 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 (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 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 (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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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 results showed that the first node death time of the data fusion algorithm is delayed to 1122 rounds, which is 391 rounds and 186 rounds later than the comparison algorithm, respectively. In the same round, the remaining energy was always greater than the comparison algorithm, and the difference gradually increased. The results indicate that the proposed multi-sensor data fusion path combining fuzzy theory and neural networks has successfully improved network efficiency and node energy utilization, and extended network lifespan.
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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 similarity function, the multi-source data fusion concept is used to construct the associated real-time streaming big data fusion device, and the data deduplication results are substituted into the fusion device to obtain the data clustering result. The experiments show that the proposed algorithm has high safety factor, good data clustering accuracy, high clustering efficiency, and low energy consumption.
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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 (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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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 experimental results of this study prove that the recognition error rate of the RFID data extraction technology based on the joint Kalman filter fusion is only 2.7%. The improved KM-KL clustering algorithm also has better performance than the traditional algorithm.
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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 fluctuations. So in this study, a data fusion algorithm for optimal node tracking in agricultural wireless sensor networks is designed. By introducing the dynamic bending distance in the dynamic time warping algorithm to replace the absolute distance in the fuzzy association algorithm and combine the sensor’s own reliability and association degree as the weighted fusion weight, which improved the fuzzy association algorithm. Finally, another three algorithm were tested for multi-temperature sensor data fusion. Compare with the kalman filter, arithmetic mean and fuzzy association algorithm, the average value of the improved data fusion algorithm is 29.5703, which is close to the average value of the other three algorithms, indicating that the data distribution is more even. Its extremely bad value is 8.9767, which is 10.04%, 1.14% and 9.85% smaller than the other three algorithms, indicating that it is more robust when dealing with outliers. Its variance is 2.6438, which is 2.82%, 0.65% and 0.27% smaller than the other three algorithms, indicating that it is more stable and has less data volatility. The results show that the algorithm proposed in this study has higher fusion accuracy and better robustness, which can obtain the fusion value that truly feedbacks the agricultural environment conditions. It reduces production costs by reducing redundant monitoring devices, the energy consumption and improves the data collection efficiency in wireless sensor networks.
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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 experiment of this article, the accuracy of the neural network before the improvement was 4.1%. With the development of society, the traditional algorithm finally dropped to 1.3%. The accuracy of the multisensor information fusion algorithm before the improvement was 3.1% at the beginning; with the development of society, it finally dropped to 1%; it can be known that the accuracy of the improved neural network is 4.6%, and with continuous improvement, it finally increased to 9.8%. The improved multisensor information fusion algorithm is the same, the accuracy at the beginning was 3.9%, and gradually increased to 9.5%. From this set of data, it can be known that the improved convolutional neural network (CNN) algorithm, and the improved multisensor information fusion algorithm should be used to study AI algorithms.
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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 management efficiency and satisfaction of school population under different algorithms, and understands the data fusion and construction under different algorithms. According to the results, decision-making tree and artificial neural network algorithms were more efficient than traditional methods in building regional digitization, and their magnitude was about 60% higher. More importantly, the machine learning-based methods in multisource heterogeneous data fusion have been better than traditional calculation methods both in computational efficiency and misleading rate with respect to false alarms and missed alarms. This shows that machine learning methods can play an important role in the analysis of multisource heterogeneous data fusion in regional digital construction.
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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 diverse and have their own advantages and disadvantages. Data analysis algorithms help urban management in areas such as spatial analysis and deep learning. Algorithm collaboration can improve decision-making accuracy and efficiency and promote the rational allocation of urban resources. In the future, algorithm development will focus on data quality, real-time, deep mining, interdisciplinary research, privacy protection, and collaborative application expansion, providing strong support for the sustainable development of smart cities.
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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 conventional filtering algorithms are improved only under this fixed and limited frame. The mixed norm-based data fusion algorithm is rarely discussed. In this paper, a mixed norm-based data fusion algorithm is proposed, and the hypothesis test statistics are constructed and adopted based on the chi-square distribution. Using the land vehicle data collected through the multi-GNSS and the IMU (Inertial Measurement Unit), the proposed algorithm is tested and compared with the conventional filtering algorithms. Results show that the influences of the outlying measurements and the uncertain noises are weakened with the proposed data fusion algorithm, and the precision of the estimates is further improved. Meanwhile, the proposed algorithm provides an open issue for geodetic applications with mixed norms.
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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 the multisensor data fusion of multisensor data fusion personalization features this four aspects, which is under the background of the sensor data fusion of the practice for college English translation activity. Firstly, the theory of data fusion estimation is elaborated, and various data fusion structures in multisensor systems are summarized. Then, the data fusion estimation model based on Kalman filter is established, and the Kalman filtering algorithms of centralized, sequential, parallel, and joint structures are given, respectively. Simulation experiments are carried out on the algorithms. Experimental results show that the estimation accuracy of the system can be improved by multisensor data fusion. Then a rule-based lexical analyzer is designed. Combined with the system model, a rule-based lexical analyzer and a comprehensive dictionary, the linguistic knowledge source throughout the whole machine translation process, are researched and designed. A hashing algorithm for lexical retrieval is designed, and various rules and data structures related to the lexical analyzer are described in formal language. The analysis algorithms of morphological preprocessing, morphological analysis, unincluded word processing, phrase analysis, and part of speech tagging are introduced.
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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 performance of different algorithms. By comparing and analyzing the performance of each algorithm in data fusion tasks, we can get their advantages and disadvantages in different scenarios. Among them, the extended Kalman filter algorithm is a classical algorithm based on Bayesian filtering, which can estimate the system state through recursive state estimation and covariance update. In addition, the author also uses linear regression, random forest, and other algorithms to compare. Then, the author uses several test sets to evaluate the performance of each algorithm. Through a comprehensive analysis of the MSE and MAE errors output by the algorithm, the applicability, advantages, and disadvantages of each algorithm in different scenarios are obtained.
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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 acquisition and representation are given, abandoning the research characteristics of object oriented. Then, the principle and architecture of multisensor data fusion are analyzed, and a generalized multisensor data fusion model is presented based on the JDL model. Finally, according to the multisensor data generalized fusion architecture, some related theories and methods are reviewed, and the tensor-based multisensor heterogeneous data generalized fusion algorithm is proposed, and the future work is prospected.
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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 robot localization. A digital compass provides precise orientation feedback, promoting better particle distribution, thus reducing computational overhead. Experiments were conducted using a mobile robot in controlled environments containing obstacles distributed in diverse configurations. Comparative studies with leading algorithms, such as Manta Ray Foraging Optimization (MRFO) and Black Widow Optimization (BWO), highlighted the proposed algorithm’s ability to achieve greater path accuracy and faster convergence, even when using fewer particles. The algorithm consistently demonstrated robustness in bypassing local minima, a notable limitation of conventional bio-inspired approaches. Therefore, the proposed algorithm is a promising solution for real-time localization in resource-constrained environments, enhancing the accuracy and efficiency in the guidance of mobile robots, thus highlighting its potential for broader adoption in mobile robotics.
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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 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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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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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 fuse the two models to generate the final dimensionless penetration depth. The experiment results show that the data fusion model has good prediction accuracy both in the whole parameter space and each evaluation space.
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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, since the node embedding of the AUMA-MRL algorithm is obtained by neighborhood sampling, for new nodes in the network, the algorithm can quickly obtain the new node embedding, as well as the similarity vector between the new node and the rest of the nodes in the network, therefore, the associated users of newly added nodes in the network can be quickly mined, and the robustness of the mining algorithm of associated users in the network is enhanced. Compared with the existing classical algorithms, the recall rate of the proposed algorithm is increased by 17.5% on average, which can effectively mine the associated users in the network.
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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 using at least three anchor nodes (ANs), combined with a multiplane location estimation, suitable for 3D ad hoc environments. The novelty of the proposed algorithm is the recursive fusion technique to obtain a reliable location estimation of a node by combining noisy information from several nodes. The feasibility of the RL algorithm under several network environments was examined through analytic formulation and simulation processes. The proposed algorithm improved the location accuracy for all the scenarios analyzed. Comparing with other 3D range-based positioning algorithms, we observe that the proposed RL algorithm presents several advantages, such as a smaller number of required ANs and a better position accuracy for the worst cases analyzed. On the other hand, compared to other 3D range-free positioning algorithms, we can see an improvement by around 15.6% in terms of positioning accuracy.
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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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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-Sum protocol, and the Push-Pull protocol) to compensate for incorrect data in WSNs. More specifically, we consider a scenario where the sensor-measured data (measured by a set of independent sensor nodes) are skewed due to Gaussian noise with a various standard deviation σ, resulting in discrepancies between the measured values and the true value of observed physical quantities. Subsequently, the aforementioned algorithms are employed to mitigate this skewness in order to improve the accuracy of the measured data. In this paper, WSNs are modeled as random geometric graphs with various connectivity, and the performance of the algorithms is evaluated using two metrics (specifically, the mean square error (MSE) and the number of sent messages required for an algorithm to be completed). Based on the presented results, it is identified that all the examined algorithms can significantly suppress incorrect sensor readings (MSE without sensor fusion = −0.42 dB if σ = 1, and MSE without sensor fusion = 14.05 dB if σ = 5), and the best performance is achieved by PS in dense graphs and by GG in sparse graphs (both algorithms achieve the maximum precision MSE = −24.87 dB if σ = 1 and MSE = −21.02 dB if σ = 5). Additionally, the performance of the analyzed distributed consensus gossip algorithms is compared to the best deterministic consensus algorithm applied for the same purpose.
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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 network dynamics, where CH selection relies on residual energy and minimal communication distance; second, to enhance packet delivery ratio (PDR) through the application of a data-compression technique; and third, to mitigate the hot-spot issue, wherein sensor nodes nearest to the base station endure higher relay burdens, consequently influencing network longevity. To address this issue, mobility models provide a straightforward solution; specifically, a Random Positioning of Grid Mobility (RPGM) model is employed to alleviate the hot-spot problem. The simulation results show that the network topology incorporating the proposed MEDF algorithm effectively enhances network longevity, optimizes average energy consumption, and improves PDR. Compared to the Energy-Efficient Multiple Data Fusion (EEMDF) algorithm, the proposed algorithm demonstrates enhancements in PDR and energy efficiency, with gains of 5.2% and 7.7%, respectively. Additionally, it has the potential to extend network lifetime by 13.9%. However, the MEDF algorithm increases delay by 0.01% compared to EEMDF. The proposed algorithm is also evaluated against other algorithms, such as the tracking-anchor-based clustering method (TACM) and Energy-Efficient Dynamic Clustering (EEDC), the obtained results emphasize the MEDF algorithm’s ability to conserve energy more effectively than the other algorithms.
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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 model. In addition, three data fusion algorithms were studied and investigated. Compared to camera data in low-light environments, the fusion data significantly improved the HAR accuracy by at least 26.68%, 19.87%, and 21.92% under the data level fusion algorithm, feature level fusion algorithm, and decision level fusion algorithm, respectively. Moreover, the data level fusion algorithm also resulted in a reduction of the best misclassification rate to 2%~6%. These findings suggest that the proposed system has the potential to enhance the accuracy of HAR in low-light environments and to decrease human activity misclassification rates.
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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 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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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 mainstream framework Transformer model of the multi-sensor fusion object detection algorithm, and we also provide a comprehensive summary of the feature fusion algorithms used in multi-sensor fusion object detection, specifically focusing on the fusion of camera and LiDAR data. This article provides an overview of feature fusion’s development into feature-level fusion and proposal-level fusion, and it specifically reviews multiple related algorithms. We discuss the application of current multi-sensor object detection algorithms. In the future, with the continuous advancement of sensor technology and the development of artificial intelligence algorithms, multi-sensor fusion object detection will show great potential in more fields.
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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 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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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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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 (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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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, and anomalies, fostering a culture of innovation. The methodology involves a comprehensive literature review, knowledge enrichment, case studies, and conclusion, providing a systematic approach to understanding the intersection of open data and machine learning. The results showcase practical applications in predictive policing, healthcare resource allocation, smart traffic management, and more. Each application is supported by relevant machine learning algorithms, emphasizing their role in addressing complex challenges. The study culminates with a simplified example of predictive policing using a Support Vector Machine (SVM) algorithm, showcasing its pseudocode and decision function equation. This example illustrates how machine learning can predict crime occurrences based on patrol data and historical crime rates. Overall, this fusion marks a pivotal chapter in technological progress and societal advancement.
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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 algorithms, the CNNMDA can greatly reduce network energy consumption under the same data amount, and effectively improve the efficiency and accuracy of data fusion. In order to solve the problem that parameter synchronization takes too long in synchronous parallel, a dynamic training data allocation algorithm in multimachine synchronous parallel is proposed. Based on the computing efficiency of compute nodes, the amount of sample data to be processed by nodes will be dynamically adjusted after each iteration. This mechanism not only enables the model to be synchronized and parallel but also reduces the time of waiting for gradient updates. Finally, a comparative experiment is carried out on the Tianhe-2 supercomputer, and the experimental results show that the proposed optimization mechanism achieves the expected effect.
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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 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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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 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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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 were conducted using actual data collected from an urban environment. Comparative analysis revealed that, when utilizing the proposed algorithm, the precision of the position, velocity, and attitude of the tested land vehicle could be improved by approximately 24%, 48%, and 35%, respectively. Furthermore, a series of filtering algorithms with different weight adjustment factors was performed to test their influence on the filtering. The application of the proposed algorithm should be accompanied by an appropriate weight adjustment factor.
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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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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 in the random selection of actions and actions with the highest reward value. It effectively improves the defects of traditional Q-learning algorithm that it is easy to fall into the local optimal and has slow convergence speed. The experimental results show that compared with other advanced fusion algorithms, the proposed method can greatly reduce the network energy consumption and effectively improve the efficiency and accuracy of data fusion under the same data volume.
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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 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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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 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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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 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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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 (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.