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- Guo, Zhiming, et al.
(author)
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Quantitative detection of apple watercore and soluble solids content by near infrared transmittance spectroscopy
- 2020
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In: Journal of Food Engineering. - : ELSEVIER SCI LTD. - 0260-8774 .- 1873-5770. ; 279
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Journal article (peer-reviewed)abstract
- Near-infrared (NIR) spectroscopy as an emerging analytical technique was used for the first time to quantitatively detect the watercore degree and soluble solids content (SSC) in apple. To reduce the data processing time and meet the needs of practical application, the variable selection methods including synergy interval (SI), successive projections algorithm (SPA), genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) were used to identify the characteristic variables and simplify the models. The spectral variables closely related to the apple bioactive components were used for the establishment of the partial least squares (PLS) models. The predictive correlation coefficient (R-p), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) were used to estimate the performance of the models. The CARS-PLS models displayed the best prediction performance using 600-1000 nm spectra with R-p, RMSEP, and RPD values of 0.9562, 1.340% and 3.720 for apple watercore degree; 0.9808, 0.327 (o)Bx and 4.845 for apple SSC, respectively. These results demonstrate the potential of the NIR transmittance spectroscopy technology for quantitative detection of SSC and watercore degree in apple fruit.
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- Liu, Jianan, et al.
(author)
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Deep Instance Segmentation with Automotive Radar Detection Points
- 2023
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In: IEEE Transactions on Intelligent Vehicles. - 2379-8858. ; 8:1, s. 84-94
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Journal article (peer-reviewed)abstract
- Automotive radar provides reliable environmental perception in all-weather conditions with affordable cost, but it hardly supplies semantic and geometry information due to the sparsity of radar detection points. With the development of automotive radar technologies in recent years, instance segmentation becomes possible by using automotive radar. Its data contain contexts such as radar cross section and micro-Doppler effects, and sometimes can provide detection when the field of view is obscured. The outcome from instance segmentation could be potentially used as the input of trackers for tracking targets. The existing methods often utilize a clustering-based classification framework, which fits the need of real-time processing but has limited performance due to minimum information provided by sparse radar detection points. In this paper, we propose an efficient method based on clustering of estimated semantic information to achieve instance segmentation for the sparse radar detection points. In addition, we show that the performance of the proposed approach can be further enhanced by incorporating the visual multi-layer perceptron. The effectiveness of the proposed method is verified by experimental results on the popular RadarScenes dataset, achieving 89.53% mean coverage and 86.97% mean average precision with the IoU threshold of 0.5, which is superior to other approaches in the literature. More significantly, the consumed memory is around 1MB, and the inference time is less than 40ms, indicating that our proposed algorithm is storage and time efficient. These two criteria ensure the practicality of the proposed method in real-world systems.
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- Ma, Jiang-Jiang, et al.
(author)
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Ultralow thermal conductivity and anisotropic thermoelectric performance in layered materials LaMOCh (M = Cu, Ag; Ch = S, Se)
- 2022
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In: Physical Chemistry, Chemical Physics - PCCP. - : Royal Society of Chemistry. - 1463-9076 .- 1463-9084. ; 24:35, s. 21261-21269
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Journal article (peer-reviewed)abstract
- In layered materials with the stacking axis perpendicular to the basal plane, anharmonicity strongly affects phonon propagation due to weak interlayer coupling, which is helpful to reduce the lattice thermal conductivity and improve the thermoelectric (TE) performance significantly. By combining first-principles calculations and the Boltzmann transport equation, we systematically analyzed and evaluated the lattice thermal conductivity and TE properties of LaMOCh (M = Cu, Ag; Ch = S, Se). The results indicate that these layered materials exhibit ultralow lattice thermal conductivities of 0.24-0.37 W m(-1) K-1 along the interlayer direction at room temperature. The low lattice thermal conductivities have been analyzed from some inherent phonon properties, such as low acoustic phonon group velocity, large Gruneisen parameters, and a short phonon relaxation time. Originating from their natural layered crystal structure, the thermal and electronic transports (i.e., thermal conductivity, Seebeck coefficient, and electrical conductivity) are both highly anisotropic between their intralayer and interlayer directions. Finally, we obtained ZT values of 1.17 and 1.26 at 900 K along the interlayer direction for n-type LaCuOSe and LaAgOSe, respectively. Generally, LaMOSe exhibit larger anisotropy than LaMOS, in both n- and p-types of doping. Our findings of low thermal conductivities and large anisotropic TE performances of these layered systems should stimulate much attention in BiCuOSe and alike layered TE families.
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- Aad, G, et al.
(author)
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- 2015
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swepub:Mat__t
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- 2019
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Journal article (peer-reviewed)
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