| 1. |
- Ding, Xinghao, et al.
(författare)
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High-resolution source localization exploiting the sparsity of the beamforming map
- 2022
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Ingår i: Signal Processing. - : Elsevier BV. - 0165-1684. ; 192
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Tidskriftsartikel (refereegranskat)abstract
- Beamforming technology plays a significant role in source localization and quantification. As traditional delay-and-sum beamformers generally yield low spatial resolution, as well as suffer from the occurrence of spurious sources, different forms of deconvolution methods have been proposed in the literature. In this work, we propose two approaches based on a sparse reconstruction framework combined with the use of the Fourier-based efficient implementation techniques. Numerical simulations and experimental data analysis show the effectiveness and advantages of the proposed methods.
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| 2. |
- Liang, Hao, et al.
(författare)
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Adaptive Variational Nonlinear Chirp Mode Decomposition
- 2022
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Ingår i: 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings. - 1520-6149. - 9781665405409 ; 2022-May, s. 5632-5636
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Konferensbidrag (refereegranskat)abstract
- Variational nonlinear chirp mode decomposition (VNCMD) is a recently introduced method for nonlinear chirp signal decomposition that has aroused notable attention in various fields. One limiting aspect of the method is that its performance relies heavily on the setting of the bandwidth parameter. To overcome this problem, we here propose a Bayesian implementation of the VNCMD, which can adaptively estimate the instantaneous amplitudes and frequencies of the nonlinear chirp signals, and then learn the active dictionary in a data-driven manner, thereby enabling a high-resolution time-frequency representation. Numerical example of both simulated and measured data illustrate the resulting improvement performance of the proposed method.
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| 3. |
- Liang, Hao, et al.
(författare)
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Sparse optimization for nonlinear group delay mode estimation
- 2022
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Ingår i: Journal of the Acoustical Society of America. - : Acoustical Society of America (ASA). - 0001-4966. ; 152:4, s. 2187-2203
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Tidskriftsartikel (refereegranskat)abstract
- Nonlinear group delay signals with frequency-varying characteristics are common in a wide variety of fields, for instance, structural health monitoring and fault diagnosis. For such applications, the signal is composed of multiple modes, where each mode may overlap in the frequency-domain. The resulting decomposition and forming of time-frequency representations of the nonlinear group delay modes is a challenging task. In this study, the nonlinear group delay signal is modelled in the frequency-domain. Exploiting the sparsity of the signal, we present the nonlinear group delay mode estimation technique, which forms the demodulation dictionary from the group delay. This method can deal with crossed modes and transient impulse signals. Furthermore, an augmented alternating direction multiplier method is introduced to form an efficient implementation. Numerical simulations and experimental data analysis show the effectiveness and advantages of the proposed method. In addition, the included analysis of Lamb waves as well as of a bearing signal show the method's potential for structural health monitoring and fault diagnosis.
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| 4. |
- Tu, Xiaotong, et al.
(författare)
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Adaptive sparse estimation of nonlinear chirp signals using Laplace priors
- 2024
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Ingår i: Journal of the Acoustical Society of America. - 0001-4966. ; 155:1, s. 78-93
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Tidskriftsartikel (refereegranskat)abstract
- The identification of nonlinear chirp signals has attracted notable attention in the recent literature, including estimators such as the variational mode decomposition and the nonlinear chirp mode estimator. However, most presented methods fail to process signals with close frequency intervals or depend on user-determined parameters that are often non-trivial to select optimally. In this work, we propose a fully adaptive method, termed the adaptive nonlinear chirp mode estimation. The method decomposes a combined nonlinear chirp signal into its principal modes, accurately representing each mode's time-frequency representation simultaneously. Exploiting the sparsity of the instantaneous amplitudes, the proposed method can produce estimates that are smooth in the sense of being piecewise linear. Furthermore, we analyze the decomposition problem from a Bayesian perspective, using hierarchical Laplace priors to form an efficient implementation, allowing for a fully automatic parameter selection. Numerical simulations and experimental data analysis show the effectiveness and advantages of the proposed method. Notably, the algorithm is found to yield reliable estimates even when encountering signals with crossed modes. The method's practical potential is illustrated on a whale whistle signal.
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| 5. |
- Zhang, Xiaoning, et al.
(författare)
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Smart real-time evaluation of tunnel fire risk and evacuation safety via computer vision
- 2024
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Ingår i: Safety Science. - 0925-7535. ; 177
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Tidskriftsartikel (refereegranskat)abstract
- The distribution of vehicles during a tunnel fire is a crucial factor that affects fire development and hazards, as well as the following evacuation and rescue operations. This work proposed a novel method using computer vision for assessing the real-time tunnel fire risk and evacuation safety by considering the classification and entry flow of vehicles. The proposed system utilizes YOLOv7 and DeepSORT for vehicle detection, classification, and tracking to enable a real-time digital twin for tunnel fire safety management. Vehicles are divided into 10 categories, in terms of their size, usage, number of passengers, fuel load, and peak fire HRR. After monitoring the vehicle flow at the tunnel portals, the real-time vehicle and fire load distribution are predicted. Then, the real-time tunnel fire scenarios and the safety of the evacuation process are evaluated based on the distribution of vehicles. The system is demonstrated in real road tunnels with traffic video cameras and exhibits a robust performance. The proposed vision-based real-time tunnel fire risk evaluation enables intelligent daily fire safety management and supports fire emergency response and decision-making.
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