Analysis of heteroscedastic measurement data by the self-refining method of interval fusion with preference aggregation – IF&amp;PA

Muravyov, Sergey V.; Khudonogova, Liudmila I.; Ho, Minh Dai

doi:10.1016/j.measurement.2021.109851

Cited by 7 publications

(2 citation statements)

References 17 publications

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“…In data-driven applications, robust and accurate data completion is of great importance [1]. Data fusion, achieved by combining data from multiple sources, can decrease the uncertainty of results [2][3][4][5]. For instance, Roy et al improved vehicle tracking and detection performance under non-line-of-sight (NLOS) image and non-image modalities by combining multiple state-of-the-art fusion strategies [6].…”

Section: Introductionmentioning

confidence: 99%

A Novel Truncated Normal Tensor Completion Method for Multi-Source Fusion Data

Zhao

2024

Mathematics

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Completing traffic data is a basic requirement for intelligent transportation systems. However, completing spatiotemporal traffic data poses a significant challenge, especially for high-dimensional data with complex missing mechanisms. Various completion methods targeting different missing mechanisms have showcased the superiority of tensor learning by effectively characterizing intricate spatiotemporal correlations. In this study, a novel tensor completion framework, known as the multi-source tensor completion method for data fusion, is proposed. This framework incorporates passenger transfer relationships between buses and subways into subway data completion, enhancing the data completion accuracy. Moreover, by combining bus transfer passenger flow data with other data dimensions, such as the different road sections, time intervals, and days, an innovative 4D low-rank tensor completion data framework was obtained. In addition, to boost the completion accuracy, a truncated l2,p norm optimization model was derived. This model enhances the non-convex performance of the objective function throughout the tensor completion process. The results highlight the superiority of the proposed completion method, leveraging fused subway/bus data over other completion methods that rely solely on subway data.

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Section: Introductionmentioning

confidence: 99%

A Novel Truncated Normal Tensor Completion Method for Multi-Source Fusion Data

Zhao

2024

Mathematics

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“…The performance of feed forward neural network and multiple regression are compared in the presence of heteroscedasticity in simulated data in [5] while heteroscedasticity was accommodated in allometric models to predict the forest biomass in [6]. The interval fusion with preference aggregation procedure is applied to process the heteroscedastic measured direct control (DC) voltage and resistance data in [7]. Also, the effect on clustering  ISSN: 2088-8708 Int J Elec & Comp Eng, Vol.…”

Section: Introductionmentioning

confidence: 99%

Scedasticity descriptor of terrestrial wireless communications channels for multipath clustering datasets

Blanza,

Trinidad,

Materum

2023

IJECE

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Fifth-generation (5G) wireless systems increased the bandwidth, improved the speed, and shortened the latency of communications systems. Various channel models are developed to study 5G. These channel models reproduce the stochastic properties of multiple-input multiple-output (MIMO) antennas by generating wireless multipath components (MPCs). The MPCs that have similar properties in delay, angles of departure, and angles of arrival form clusters. The multipaths and multipath clusters serve as datasets to understand the properties of 5G. These datasets generated by the Cooperation in Science and Technology 2100 (COST 2100), International Mobile Telecommunications-2020 (IMT-2020), quasi deterministic radio channel generator (QuaDRiGa), and wireless world initiative new radio II (WINNER II) channel models are tested for their homoscedasticity based on Johansen's procedure. Results show that the COST 2100, QuaDRiGa, and WINNER II datasets are heteroscedastic, while the IMT-2020 dataset is homoscedastic.

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Method of interval fusion with preference aggregation in brightness thresholds selection for automatic weld surface defects recognition

Muravyov,

Nguyen

2024

Measurement

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Analysis of heteroscedastic measurement data by the self-refining method of interval fusion with preference aggregation – IF&PA

Cited by 7 publications

References 17 publications

A Novel Truncated Normal Tensor Completion Method for Multi-Source Fusion Data

A Novel Truncated Normal Tensor Completion Method for Multi-Source Fusion Data

Scedasticity descriptor of terrestrial wireless communications channels for multipath clustering datasets

Method of interval fusion with preference aggregation in brightness thresholds selection for automatic weld surface defects recognition

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