A New View of Multisensor Data Fusion: Research on Generalized Fusion

Chen, Guo; Liu, Zhigui; Yu, Guang; Liang, Jianhong

doi:10.1155/2021/5471242

Cited by 10 publications

(5 citation statements)

References 127 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to achieve an enhanced predictive model [ 23 ], this study integrated feature-level data fusion of variable-selected VIS-NIR information with image data. The emphasis in feature-level fusion is on extracting feature-variable data from different data sources and combining them into a new array to create the optimal model.…”

Section: Methodsmentioning

confidence: 99%

Online System for Monitoring the Degree of Fermentation of Oolong Tea Using Integrated Visible–Near-Infrared Spectroscopy and Image-Processing Technologies

Zheng,

Solomon Adade,

Rong

et al. 2024

Foods

View full text Add to dashboard Cite

During the fermentation process of Oolong tea, significant changes occur in both its external characteristics and its internal components. This study aims to determine the fermentation degree of Oolong tea using visible–near–infrared spectroscopy (vis-VIS-NIR) and image processing. The preprocessed vis-VIS-NIR spectral data are fused with image features after sequential projection algorithm (SPA) feature selection. Subsequently, traditional machine learning and deep learning classification models are compared, with the support vector machine (SVM) and convolutional neural network (CNN) models yielding the highest prediction rates among traditional machine learning models and deep learning models with 97.14% and 95.15% in the prediction set, respectively. The results indicate that VIS-NIR combined with image processing possesses the capability for rapid non-destructive online determination of the fermentation degree of Oolong tea. Additionally, the predictive rate of traditional machine learning models exceeds that of deep learning models in this study. This study provides a theoretical basis for the fermentation of Oolong tea.

show abstract

Section: Methodsmentioning

confidence: 99%

Online System for Monitoring the Degree of Fermentation of Oolong Tea Using Integrated Visible–Near-Infrared Spectroscopy and Image-Processing Technologies

Zheng,

Solomon Adade,

Rong

et al. 2024

Foods

View full text Add to dashboard Cite

show abstract

“…Hall and Linas [15] proposed a definition of data fusion in which data from multiple sources are processed and combined based on relevant information to obtain higher accuracy and greater interpretability than single-source data. On the one hand, researchers have conducted a large number of studies related to multimodal data fusion, involving fusion strategies [5], data processing methods [1], multisensor data fusion [15], information fusion [28], and data fusion applications [24], etc. Meanwhile, some technical engineering and methodological guidelines on data fusion are provided in related studies [11,30].…”

Section: Multi-channel Data Fusionmentioning

confidence: 99%

Multi-channel Sensor Network Construction, Data Fusion and Challenges for Smart Home

Zhang,

Ananda,

et al. 2023

Proceedings of the Eleventh International Symposium of Chinese CHI

View full text Add to dashboard Cite

Both sensor networks and data fusion are essential foundations for developing the smart home Internet of Things (IoT) and related fields. We proposed a multi-channel sensor network construction method involving hardware, acquisition, and synchronization in the smart home environment and a smart home data fusion method (SHDFM) for multi-modal data (position, gait, voice, pose, facial expression, temperature, and humidity) generated in the smart home environment to address the configuration of a multi-channel sensor network, improve the quality and efficiency of various human activities and environmental data collection, and reduce the difficulty of multi-modal data fusion in the smart home. SHDFM contains 5 levels, with inputs and outputs as criteria to provide recommendations for multi-modal data fusion strategies in the smart home. We built a real experimental environment using the proposed method in this paper. To validate our method, we created a real experimental environment -a physical setup in a home-like scenario where the multi-channel sensor network and data fusion techniques were deployed and evaluated. The acceptance and testing results show that the proposed construction and data fusion methods can be applied to the examples with high robustness, replicability, and scalability. Besides, we discuss how smart homes with multi-channel sensor networks can support digital twins.

show abstract

“…In this review, we identify the research gaps that have not been answered by existing reviews on data fusion in metrology. There are numerous existing reviews on data fusion and their applications, such as [6,25,30,31] applied in general engineering and computer science contexts. In the specific field of coordinate metrology, reviews such as [15] explain how data fusion has been employed to fuse geometric data of engineered parts, particularly in the alignment of multiple measurements (i.e.…”

Section: Research Questionsmentioning

confidence: 99%

Applications of data fusion in optical coordinate metrology: a review

Zhang

Catalucci

Thompson

et al. 2022

Int J Adv Manuf Technol

View full text Add to dashboard Cite

Data fusion enables the characterisation of an object using multiple datasets collected by various sensors. To improve optical coordinate measurement using data fusion, researchers have proposed numerous algorithmic solutions and methods. The most popular examples are the Gaussian process (GP) and weighted least-squares (WLS) algorithms, which depend on user-defined mathematical models describing the geometric characteristics of the measured object. Existing research on GP and WLS algorithms indicates that GP algorithms have been widely applied in both academia and industry, despite their use being limited to applications on relatively simple geometries. Research on WLS algorithms is less common than research on GP algorithms, as the mathematical tools used in the WLS cases are too simple to be applied with complex geometries. Machine learning is a new technology that is increasingly being applied to data fusion applications. Research on this technology is relatively scarce, but recent work has highlighted the potential of machine learning methods with significant results. Unlike GP and WLS algorithms, machine learning algorithms can autonomously learn the geometrical features of an object. To understand existing research in-depth and explore a path for future work, a new taxonomy of data fusion algorithms is proposed, covering the mathematical background and existing research surrounding each algorithm type. To conclude, the advantages and limitations of the existing methods are reviewed, highlighting the issues related to data quality and the types of test artefacts.

show abstract

A New View of Multisensor Data Fusion: Research on Generalized Fusion

Cited by 10 publications

References 127 publications

Online System for Monitoring the Degree of Fermentation of Oolong Tea Using Integrated Visible–Near-Infrared Spectroscopy and Image-Processing Technologies

Online System for Monitoring the Degree of Fermentation of Oolong Tea Using Integrated Visible–Near-Infrared Spectroscopy and Image-Processing Technologies

Multi-channel Sensor Network Construction, Data Fusion and Challenges for Smart Home

Applications of data fusion in optical coordinate metrology: a review

Contact Info

Product

Resources

About