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- 2019
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Tidskriftsartikel (refereegranskat)
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- Kristan, Matej, et al.
(författare)
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The Visual Object Tracking VOT2016 Challenge Results
- 2016
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Ingår i: COMPUTER VISION - ECCV 2016 WORKSHOPS, PT II. - Cham : SPRINGER INT PUBLISHING AG. - 9783319488813 - 9783319488806 ; , s. 777-823
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Konferensbidrag (refereegranskat)abstract
- The Visual Object Tracking challenge VOT2016 aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 70 trackers are presented, with a large number of trackers being published at major computer vision conferences and journals in the recent years. The number of tested state-of-the-art trackers makes the VOT 2016 the largest and most challenging benchmark on short-term tracking to date. For each participating tracker, a short description is provided in the Appendix. The VOT2016 goes beyond its predecessors by (i) introducing a new semi-automatic ground truth bounding box annotation methodology and (ii) extending the evaluation system with the no-reset experiment.
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- Gao, Zhi xin, et al.
(författare)
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Progress of passive enhanced heat transfer tubes
- 2017
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Ingår i: Xiandai Huagong/Modern Chemical Industry. - 0253-4320. ; 37:3, s. 24-30
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Forskningsöversikt (refereegranskat)abstract
- A review of several typical enhanced heat transfer tubes such as corrugated tube, transversally corrugated tube, twisted tube and spiral fluted tube, is carried out. Compared with smooth wall tube, these enhanced tubes have higher heat exchange capacity and better anti - fouling ability. Besides, the heat transfer efficiency can be promoted to a higher level if the objects that can disturb flow are put in the tubes. However, higher heat transfer efficiency of such enhanced heat transfer tubes can only be achieved under certain conditions. Therefore, new heat transfer tubes that could avoid the formation of mobile dead-zone should be designed to meet the broad applications or facilitate better heat transfer effect.
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- Gao, Xiang, et al.
(författare)
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Planting Age Identification and Yield Prediction of Apple Orchard Using Time-Series Spectral Endmember and Logistic Growth Model
- 2023
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Ingår i: Remote Sensing. - : MDPI AG. - 2072-4292. ; 15:3, s. 642-642
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Tidskriftsartikel (refereegranskat)abstract
- In response to significant shifts in dietary and lifestyle preferences, the global demand for fruits has increased dramatically, especially for apples, which are consumed worldwide. Growing apple orchards of more productive and higher quality with limited land resources is the way forward. Precise planting age identification and yield prediction are indispensable for the apple market in terms of sustainable supply, price regulation, and planting management. The planting age of apple trees significantly determines productivity, quality, and yield. Therefore, we integrated the time-series spectral endmember and logistic growth model (LGM) to accurately identify the planting age of apple orchard, and we conducted planting age-driven yield prediction using a neural network model. Firstly, we fitted the time-series spectral endmember of green photosynthetic vegetation (GV) with the LGM. By using the four-points method, the environmental carrying capacity (ECC) in the LGM was available, which serves as a crucial parameter to determine the planting age. Secondly, we combined annual planting age with historical apple yield to train the back propagation (BP) neural network model and obtained the predicted apple yields for 12 counties. The results show that the LGM method can accurately estimate the orchard planting age, with Mean Absolute Error (MAE) being 1.76 and the Root Mean Square Error (RMSE) being 2.24. The strong correlation between orchard planting age and apple yield was proved. The results of planting age-driven yield prediction have high accuracy, with the MAE up to 2.95% and the RMSE up to 3.71%. This study provides a novel method to accurately estimate apple orchard planting age and yields, which can support policy formulation and orchard planning in the future.
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| 9. |
- Jin, Zhi jiang, et al.
(författare)
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Effects of pitch and corrugation depth on heat transfer characteristics in six-start spirally corrugated tube
- 2017
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Ingår i: International Journal of Heat and Mass Transfer. - : Elsevier BV. - 0017-9310. ; 108, s. 1011-1025
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Tidskriftsartikel (refereegranskat)abstract
- Spirally corrugated tube is one of the most important parts of coaxial heat exchangers. It can greatly improve the heat transfer efficiency of heat exchangers. Here, a novel spirally corrugated tube with six-start is proposed. However, up to now, there is little literature about the heat transfer performance and flow field of this novel six-start spirally corrugated tube. In this paper, the effects of geometric parameters (pitch p, corrugation depth e), Reynolds number Re and fluid properties on the heat transfer performances are investigated based on the validated numerical model. The results show that with the increasing of pitch p, both the heat transfer coefficient h and Nusselt number Nu decrease gradually. Meanwhile, with the increasing of corrugation depth e, both the secondary flow velocity vxy and the vorticity of longitudinal vortex increase gradually. Moreover, under the same working condition, the heat transfer performances of the six-start spirally corrugated tube are affected by both the working medium and Reynolds number. Finally, a criterion correlation for heat transfer calculation in the six-start spirally corrugated tube is proposed and validated to be reliable and suitable. This work can reveal the enhanced heat transfer mechanism of the six-start spirally corrugated tube and benefit the further research on heat transfer characteristics of multi-start spirally corrugated tube or other related devices.
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| 10. |
- Kristanl, Matej, et al.
(författare)
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The Seventh Visual Object Tracking VOT2019 Challenge Results
- 2019
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Ingår i: 2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW). - : IEEE COMPUTER SOC. - 9781728150239 ; , s. 2206-2241
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Konferensbidrag (refereegranskat)abstract
- The Visual Object Tracking challenge VOT2019 is the seventh annual tracker benchmarking activity organized by the VOT initiative. Results of 81 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis as well as the standard VOT methodology for long-term tracking analysis. The VOT2019 challenge was composed of five challenges focusing on different tracking domains: (i) VOT-ST2019 challenge focused on short-term tracking in RGB, (ii) VOT-RT2019 challenge focused on "real-time" short-term tracking in RGB, (iii) VOT-LT2019 focused on long-term tracking namely coping with target disappearance and reappearance. Two new challenges have been introduced: (iv) VOT-RGBT2019 challenge focused on short-term tracking in RGB and thermal imagery and (v) VOT-RGBD2019 challenge focused on long-term tracking in RGB and depth imagery. The VOT-ST2019, VOT-RT2019 and VOT-LT2019 datasets were refreshed while new datasets were introduced for VOT-RGBT2019 and VOT-RGBD2019. The VOT toolkit has been updated to support both standard short-term, long-term tracking and tracking with multi-channel imagery. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website(1).
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