Data Fusion Algorithm Research Articles

Fuzzy Integrated Network Monitoring and Perception Development of Regional Economic System

This study aims to investigate on the fuzzy integrated network monitoring and perception development of regional economic system. Since the change of some indicators in the evaluation of regional economic differences may lead to the change of the overall evaluation effect from good to bad or from bad to good, it is necessary to select the most sensitive, easy to measure and rich in connotation leading indicators among the many indicators as the evaluation indicators. According to the algorithm of fuzzy integration and fuzzy data fusion, this paper constructs a comprehensive fuzzy evaluation index system of regional economic, such as economic development scale. Based on the system, this paper studies the fuzzy integrated network monitoring and sensing method. Then, the principal component analysis method of fuzzy control is further applied. With the help of SPSS statistical software, taking the national economic development in 2021 as an example, the general method and specific steps of constructing the comprehensive evaluation index system of regional economic differences are given. The experimental results show that this method has certain reference value for the fuzzy integrated network monitoring and perception development of regional economic systems.

A fractal belief KL divergence for decision fusion

Dempster–Shafer (D–S) evidence theory is useful in the realm of multi-source data fusion. However, a counterintuitive result may be obtained when the belief probability assignments (BPAs) are highly conflicting. To overcome this flaw, in this paper a symmetric fractal-based belief Kullback–Leibler divergence (FBDSKL) is proposed. It is used to measure the divergence between BPAs, and is more capable than the existing belief divergence methods in measuring the conflict between two BPAs in numerical examples. Furthermore, the proposed FBDSKL is proved to have desirable properties including nonnegativity, nondegeneracy and symmetry. To apply FBDSKL divergence measure to practical problems, a novel FBDSKL-based multi-source data fusion (FBDSKL-MSDF) algorithm is designed. Through comparisons with the well-known related methods, the proposed FBDSKL-MSDF algorithm is validated to be superior and more robust. Finally, the proposed FBDSKL-MSDF is applied to two real-world classification problems to verify its high practicability.

Multiwireless sensors for electrical measurement based on nonlinear improved data fusion algorithm

Abstract In order to improve the accuracy of collected data and avoid table lookup, the adaptive weighted fusion algorithm is improved. According to the characteristics of the median and the mean value in the normal distribution, a new method of preprocessing to remove outliers is proposed to improve the accuracy of the final fusion result. The algorithm is used to calculate the temperature data to be processed in a greenhouse. The results showed that the fusion result after average processing was X ˆ \hat{X} = 15.77°C. The standard deviation is σ \sigma = 0.1194°C. After the treatment of the Grabbs criterion, the fusion result is X ˆ \hat{X} = 15.73°C and the standard deviation is σ \sigma = 0.1110°C. The fusion result of the improved algorithm is X ˆ \hat{X} = 15.74°C. The standard deviation is σ \sigma = 0.0959°C. Advantages of various preprocessing algorithms: improved algorithm > Grubbs method > no preprocessing. From the processing results of group A1 data, it can be seen that the improved algorithm can effectively suppress the ipsilateral shielding effect. Compared with the traditional Grubbs method to eliminate outliers and other algorithms, the improved algorithm can make the standard deviation of the fusion result smaller, and the fusion result can better represent the overall distribution, and there is no need to look up the table.

Analysis of the evolution and driving forces of tidal wetlands at the estuary of the Yellow River and Laizhou Bay based on remote sensing data cube

The rising of the “claiming land from tidal wetlands and sea” has gradually reduced tidal wetlands, resulting in a huge damage to the coastal ecological environment. It is of great significance to monitor tidal wetlands by remote sensing technology. However, due to the influence of tides and weather, monitoring tidal wetlands by remote sensing technology is often inaccurate. In this study, using dense time series images data cube constructed by the spatio-temporal data fusion algorithm, we monitored tidal wetlands at the estuary of the Yellow River and Laizhou Bay; and we analyzed their evolution and driving forces in detail. This study revealed several findings. Firstly, the area of tidal wetlands had shown a decreasing trend; the tidal wetland at the new estuary of the Yellow River had extended to the sea from 1976 to 2021; other tidal wetlands had shrunk year by year. Secondly, a more important finding was that sedimentation, erosion and reclamation had led to the changes of the tidal wetlands; and there were no obvious changing trends of the tidal wetlands' area caused by these three factors in each time period. Finally, we confirmed that salt field driving force, salt field and harbor driving force, aquaculture pond driving force and the erosion of natural factor driving force had reduced tidal wetlands; while the sedimentation of the natural factor driving force had made the tidal wetlands increase; and the newly added area of all driving forces had had no obvious regular changing trends. Our proposing to monitor tidal wetlands by using dense time series images data cube provides a general approach for tidal wetlands’ study and our results demonstrate the need for better coastal management of the estuary of the Yellow River and Laizhou Bay.

A Flexible Sensor and MIMU-Based Multisensor Wearable System for Human Motion Analysis

Motivation: Magnetic–inertial measurement units (MIMUs) and flexible sensors are widely used in the wearable measurement system for human motion monitoring, clinical gait detection, and robotics motion control. However, MIMUs demonstrate measurement error due to magnetic disturbance in the indoor environment, and flexible sensors usually have low performance on linearity and accuracy. Objective: This article is intended to eliminate the low-accuracy problem caused by magnetic disturbances and improve the measurement accuracy of MIMU–flexible-sensor-based wearable systems. Approach: 1) a three-stage real-time adaptive anti-disturbance data fusion (RT-ADF) algorithm is proposed, which contains an anti-disturbance filter based on a double Mahony filter along with a state observer, a signal holder for sensors’ data transmit synchronously, and a data fusion based on an adaptive Kalman filter; 2) the proposed algorithm is used and validated its performance on a designed MIMU–flexible sensor wearable system; and 3) ten groups of knee motions (flexion/extension), ten groups of hip motions (adduction/abduction), and ten groups of elbow motions (flexion/extension) have been done by seven subjects in the experiments. Main Results: The designed multisensor wearable system based on the presented data fusion algorithm demonstrates a high-accuracy performance under the magnetic disturbance environment, and the maximum root mean square error (RMSE) of the measured continuous 3-D motion angle of the knee, hip, and elbow cross all the experiments was 1.23°, 1.15°, and 3.67° for each axis.

A new approach of ensemble learning in fully automated identification of structural modal parameters of concrete gravity dams: A case study of the Koyna dam

This study proposes a new automatic modal identification algorithm that identifies the system's modal parameters based on output-only data. The identification process is based on a stochastic subspace algorithm. Stochastic subspace algorithms only need the model degree to identify the modal parameters of the system. A model degree is an appropriate degree of the state-space model and it is estimated by using the Singular Value Criterion. Several clustering algorithms with different approaches have been used. The most powerful data fusion algorithm, which is called Dempster-Schaffer Theory is applied for integrating the results of the algorithms and preventing divergence of results. Furthermore, it focused on measuring the uncertainty of the identified modal parameters. In the proposed modal identification algorithm, only one data set is used for identification. In this situation, the variety of data is small and limited, and statistical resampling methods seem to be a good option. Establishing the above cycle enables the identification algorithm to identify modal parameters and quantify their uncertainty based only on a data set. This algorithm uses many fields, such as data collection, signal processing techniques, model reduction, feature extraction, clustering and data fusion. The Koyna gravity dam is selected as a case study to evaluate the performance of the developed algorithm. In the first step, a comparative study is carried out to verify the developed finite element model of the Koyna dam. Afterwards, the dam is excited by the Koyna earthquake, and dynamic responses of the dam are collected from the minimum number of points required for system identification. It is noteworthy the number of modes that are identified in this work is equal to 4 vibration modes. The maximum difference between the natural frequencies identified by the present algorithm and the finite element model does not exceed 4.33%. Also, the average difference is less than two percent, which indicates the high accuracy of the system identification algorithm. Also, all the vibration modes in the desired frequency range have been correctly identified. The results indicate that modal parameters with remarkable precision have been extracted by the modified algorithm.

High-Spatial-Resolution NDVI Reconstruction with GA-ANN.

The normalized differential vegetation index (NDVI) for Landsat is not continuous on the time scale due to the long revisit period and the influence of clouds and cloud shadows, such that the Landsat NDVI needs to be filled in and reconstructed. This study proposed a method based on the genetic algorithm-artificial neural network (GA-ANN) algorithm to reconstruct the Landsat NDVI when it has been affected by clouds, cloud shadows, and uncovered areas by relying on the MODIS characteristics for a wide coverage area. According to the self-validating results of the model test, the RMSE, MAE, and R were 0.0508, 0.0557, and 0.8971, respectively. Compared with the existing research, the reconstruction model based on the GA-ANN algorithm achieved a higher precision than the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the flexible space-time data fusion algorithm (FSDAF) for complex land use types. The reconstructed method based on the GA-ANN algorithm had a higher root mean square error (RMSE) and mean absolute error (MAE). Then, the Sentinel NDVI data were used to verify the accuracy of the results. The validation results showed that the reconstruction method was superior to other methods in the sample plots with complex land use types. Especially on the time scale, the obtained NDVI results had a strong correlation with the Sentinel NDVI data. The correlation coefficient (R) of the GA-ANN algorithm reconstruction's NDVI and the Sentinel NDVI data was more than 0.97 for the land use types of cropland, forest, and grassland. Therefore, the reconstruction model based on the GA-ANN algorithm could effectively fill in the clouds, cloud shadows, and uncovered areas, and produce NDVI long-series data with a high spatial resolution.

Construction of Digital Art Education Platform under the “Internet+” Environment

With the development of society and the rapid development of the Internet, there are currently about 100 million web pages and 100 million hyperlinks, and in the future, the number of web pages and hyperlinks will also intensify. How to make this huge Internet better used by people has become a common concern of the international community. In recent years, with the acceleration of the reform of China’s basic education curriculum, people have become more and more aware of the special status and superiority of art education in overall education, and the general consensus that “lack of art education is an incomplete education.” Therefore, this paper proposes the construction of a platform for digital art education under the “Internet+” environment. This paper first introduces how to perform data mining in the Internet era, and proposes an interactive data fusion algorithm and model for Internet and crowdsourcing. Then, a statistical analysis was made on the preschool art education syllabus of 20 colleges and universities in different provinces and cities. The experimental results have shown that there are eight undergraduate colleges and universities that have not written syllabuses for College Aesthetic Education majors in art courses, and they still account for 40% of the total sample size. This shows that some Chinese colleges and universities have not paid enough attention to the art courses of College Aesthetic Education majors, have loopholes in curriculum management, and neglected teaching staff.

An RGB-D camera-based indoor occupancy positioning system for complex and densely populated scenarios

The dynamic changes in occupants’ presence, movements and other behaviours could affect the actual operation and energy consumption of buildings. The design and operation of buildings can be adjusted to be more energy efficient by thinking over the actual distribution of occupants and application scenarios. Nevertheless, accurate, nonintrusive and applicable occupancy positioning systems, especially for complex and densely populated scenarios, have not yet been established. Herein, we propose a novel indoor positioning system based on a red green blue-depth (RGB-D) camera (CIOPS-RGBD). This system utilizes multiple RGB-D cameras to capture colour and depth images from different views. A data fusion algorithm is developed according to the result from a human pose estimation method. Then, the proposed CIOPS-RGBD system was setup and verified within a multifunction room for steady and dynamic accuracy estimation. The accuracy was verified within 10 cm in most cases. Finally, the system was tested under different application scenarios with more than 25 occupants in an 86 m2 space. The results demonstrate that the system can provide high-quality occupancy positioning and body orientation information for these scenarios in almost real-time, providing a solid basis to improve the actual operation and design of indoor environment creation systems.

Multi-Sensor Data Fusion for Real-Time Multi-Object Tracking

Sensor data fusion is essential for environmental perception within smart traffic applications. By using multiple sensors cooperatively, the accuracy and probability of the perception are increased, which is crucial for critical traffic scenarios or under bad weather conditions. In this paper, a modular real-time capable multi-sensor fusion framework is presented and tested to fuse data on the object list level from distributed automotive sensors (cameras, radar, and LiDAR). The modular multi-sensor fusion architecture receives an object list (untracked objects) from each sensor. The fusion framework combines classical data fusion algorithms, as it contains a coordinate transformation module, an object association module (Hungarian algorithm), an object tracking module (unscented Kalman filter), and a movement compensation module. Due to the modular design, the fusion framework is adaptable and does not rely on the number of sensors or their types. Moreover, the method continues to operate because of this adaptable design in case of an individual sensor failure. This is an essential feature for safety-critical applications. The architecture targets environmental perception in challenging time-critical applications. The developed fusion framework is tested using simulation and public domain experimental data. Using the developed framework, sensor fusion is obtained well below 10 milliseconds of computing time using an AMD Ryzen 7 5800H mobile processor and the Python programming language. Furthermore, the object-level multi-sensor approach enables the detection of changes in the extrinsic calibration of the sensors and potential sensor failures. A concept was developed to use the multi-sensor framework to identify sensor malfunctions. This feature will become extremely important in ensuring the functional safety of the sensors for autonomous driving.

Understanding spatiotemporal evolution of the surface urban heat island in the Bangkok metropolitan region from 2000 to 2020 using enhanced land surface temperature

The urbanization process has significantly intensified surface urban heat island (SUHI) effects in the Bangkok Metropolitan Region (BMR). However, understanding the evolution of the urban thermal environment is challenging due to the difficulty in obtaining consistent remote sensing data of the cloud-prone landscape in the BMR. In this study, the data fusion algorithm was utilized to fill cloud-induced data gap and create high spatiotemporal-resolution data by blending Landsat and MODIS remote sensing images. The fused data was used to retrieve land surface temperature (LST) for winter months from 2000 to 2020. The spatiotemporal variations in SUHI were then captured using spatial cluster analysis. Finally, gradient analysis and geographically weighted regression (GWR) were employed to analyse the effects of land cover composition on LST. The SUHI intensity in winter increased from 4.40 °C in 2000 to 5.76 °C in 2020. The areal percentage of SUHI hot spots increased from 24.86% to 29.13%. The gradient analysis results indicated that vegetation with a density higher than 0.3 had a significant effect on LST compared to low-density areas. The woody lands were more effective in lowering LST than cultivated lands. These results provide useful information for developing heat mitigation strategies in the metropolitan regions.

Prediction and monitoring model for farmland environmental system using soil sensor and neural network algorithm

Abstract In this study, data fusion algorithm is used to classify the soil species and calibrate the soil humidity sensor, and by using edge computing and a wireless sensor network, farmland environment monitoring system with a two-stage calibration function of frequency domain reflectometer (FDR) is established. Edge computing is used in system nodes, including the saturation value of the soil humidity sensor, the calculated soil hardness, the calculation process of the neural network, and the model of soil classification. A bagged tree is adopted to avoid over-fitting to reduce the prediction variance of the decision tree. A decision tree model is established on each training set, and the C4.5 algorithm is adopted to construct each decision tree. After primary calibration, the root mean squared error (RMSE) between the measured and standard values is reduced to less than 0.0849%. The mean squared error (MSE) and mean absolute error (MAE) are reduced to less than 0.7208 and 0.6929%. The bagged tree model and backpropagation neural network are used to classify the soil and train the dynamic soil dataset. The output of the trained neural network is closer to the actual soil humidity than that of the FDR soil humidity sensor. The MAE, the MSE, and the RMSE decrease by 1.37%, 3.79, and 1.86%. With accurate measurements of soil humidity, this research shows an important guiding significance for improving the utilization efficiency of agricultural water, saving agricultural water, and formulating the crop irrigation process.

Development and Practice of Sports-Related Public Welfare Platform Based on Multi-Sensor Technology

Today, more and more Internet public media platforms allowing people to make donations or seek help are being founded in China. However, there are few specialized sports-related public welfare platforms. In this paper, a sports-related public welfare platform that aims to help people who were disabled due to participation in sports and those who are disabled but want to participate in sports was developed based on multi-sensor technology. A multi-sensor data fusion algorithm was developed, and its estimation performance was verified by comparing it with the existing Kalman consistent filtering algorithm in terms of average estimation and average consistency errors. Experimental results prove that the speed of the data collection and analysis of the sports-related public welfare platform using the algorithm established in this paper was greatly improved. Relevant data on how users used this platform showed that various factors affected users' practical satisfaction with sports-related public welfare media platforms. It is suggested that a sports-related public welfare media platform should pay attention to the aid effect, and specific efforts should be devoted to improving the reliability and timeliness of public welfare aid information, and ensuring the stability of the platform system.

Joint torques estimation in human gait based on Gaussian process.

Human gait involves activities in nervous and musculoskeletal dynamics to modulate joint torques with time continuously for adapting to varieties of walking conditions. The goal of this paper is to estimate the joint torques of lower limbs in human gait based on Gaussian process. The potential uses of this study include optimization of exoskeleton assistance, control of the active prostheses, and modulating the joint torque for human-like robots. To achieve this, Gaussian process (GP) based data fusion algorithm is established with joint angles as the inputs. The statistic nature of the proposed model can explore the correlations between joint angles and joint torques, and enable accurate joint-torque estimations. Experiments were conducted for 5 subjects at three walking speed (0.8 m/s, 1.2 m/s, 1.6m/s). The results show that it is possible to estimate the joint torques at different scenarios.

A Data Fusion Algorithm for Digital Twin of Controlled Axes: Development, Implementation and Test on Machine

This work aims to propose an original method for representing signals collected by position and acceleration sensors and their processing in order to obtain a reliable reconstruction of the trajectory performed. The double benefit obtained consists in the improvement of the estimation of the position reached by controlled axes and in the reduction of the data to represent the available information. Various approximation algorithms using B-spline have been tested through numerical tests. A data fusion algorithm was then identified capable of combining several independent sources, profoundly different in their characteristics (frequency, spectral density, signal/noise ratio), paying particular attention to eliminating drift error caused by the integration. The research was completed with a first phase of experimental validation which resulted in the fusion of signals from three different sources (encoder, camera and accelerometers).

Artificial intelligence for medicine: Progress, challenges, and perspectives

<p>Artificial Intelligence (AI) has transformed how we live and how we think, and it will change how we practice medicine. With multimodal big data, we can develop large medical models that enables what used to unimaginable, such as early cancer detection several years in advance and effective control of virus outbreaks without imposing social burdens. The future is promising, and we are witnessing the advancement. That said, there are challenges that cannot be overlooked. For example, data generated is often isolated and difficult to integrate from both perspectives of data ownership and fusion algorithms. Additionally, existing AI models are often treated as black boxes, resulting in vague interpretation of the results. Patients also exhibit a lack of trust to AI applications, and there are insufficient regulations to protect patients�� privacy and rights. However, with the advancement of AI technologies, such as more sophisticated multimodal algorithms and federated learning, we may overcome the barriers posed by data silos. Deeper understanding of human brain and network structures can also help to unravel the mysteries of neural networks and construct more transparent yet more powerful AI models. It has become something of a trend that an increasing number of clinicians and patients will implement AI in their life and medical practice, which in turn can generate more data and improve the performance of models and networks. Last but not the least, it is crucial to monitor the practice of AI in medicine and ensure its equity, security, and responsibility.</p>

Multi-Level Fusion Optimization in Cyber-Physical Systems Using Computer Vision-Based Fault Detection

The healthcare sector's use of cyber-physical systems to provide high-quality patient treatment highlights the need for sophisticated security solutions due to the wide range of attack surfaces from medical and mobile devices, as well as body sensor nodes. Cyber-physical systems have various processing technologies to choose from, but these technical methods are as varied. Existing technologies are not well-suited for managing complex information about problem identification and diagnosis, which is distinct from technology. To address this issue, intelligent techniques for fusion processing, such as multi-sensor fusion system architectures and fusion optimization, can be used to improve fusion score and decision-making. Additionally, the use of deep learning models and multimedia data fusion applications can help to combine multiple models for intelligent systems and enhance machine learning for data fusion in E-Systems and cloud environments. Fuzzy approaches and optimization algorithms for data fusion can also be applied to robotics and other applications.. In this paper, a computer vision technology-based fault detection (CVT-FD) framework has been suggested for securely sharing healthcare data. When utilizing a trusted device like a mobile phone, end-users can rest assured that their data is secure. Cyber-attack behavior can be predicted using an artificial neural network (ANN), and the analysis of this data can assist healthcare professionals in making decisions. The experimental findings show that the model outperforms with current detection accuracy (98.3%), energy consumption (97.2%), attack prediction (96.6%), efficiency (97.9%), and delay ratios (35.6%) over existing approaches.

Improving the Transmission Efficiency of a WSN with the IACO Algorithm

The goal of this study is to reduce the energy consumption of the sensing network and enhance the overall life cycle of the network. This study proposes a data fusion algorithm for wireless sensor networks based on improved ant colony optimization (IACO) to reduce the amount of data transmitted by wireless sensor networks (WSN). This study updates pheromones for multiple optimal routes to improve the global optimal route in search function. The algorithm proposed in this study can reduce node energy consumption, improve network load balancing and prolong network life cycle. Through data fusion, regression analysis model and information processing of each node, this study uses an improved ant colony algorithm to identify the transferals avoid superfluous energy waste caused by long-span network transferal, set the shortest route and transmit data to the central node. The algorithm proposed in this study is conducive to improving the life cycle and stable network, that is, the most suitable and effective way to improve the energy consumption rate of the sensing nodes.

Robust Adaptive Heading Tracking Fusion for Polarization Compass With Uncertain Dynamics and External Disturbances

For the polarization compass (PC), maintaining high-precision and high-robustness autonomous measurement of the heading information under conditions where the atmospheric polarized light is being blocked or disturbed represents a challenging problem. In this paper, the E-vector orientation algorithm, which provides high orientation accuracy and real-time performance in sunny weather, and the symmetric axis orientation algorithm, which offers a strong anti-interference ability in occluded environments, are integrated to solve this problem. Furthermore, an improved state error model is established to increase the accuracy of the heading estimation. To track the system state rapidly and circumvent the uncertainties caused by measurement noise variance, an improved variational Bayesian strong tracking cubature Kalman filter (VBSTCKF) is proposed to estimate both the state and the time-varying measurement noise variance accurately. In addition, a multi-frequency data fusion algorithm based on residual compensation is proposed to overcome the inconsistent sampling operation frequency problems of the two orientation algorithms. The outstanding feature is that the proposed multi-frequency VBSTCKF (MF-VBSTCKF) can improve the orientation precision and robustness while also ensuring the high-frequency output performance. Finally, experimental results acquired in occluded environments verify the superiority of the proposed method.

Retracted: Data Fusion Algorithm for Myocardial Proteomics and Its Research in Sports.

[This retracts the article DOI: 10.1155/2022/4049169.].