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- 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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| 2. |
- Cheng, Li, et al.
(författare)
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Online Identification of Wind Farm Wide Frequency Admittance with Power Cables Using the Artificial Neural Network
- 2023
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Ingår i: 2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 1530-1535
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Konferensbidrag (refereegranskat)abstract
- In power-electronic-based power systems like wind farms, stability analysis requires knowledge of system impedance across a wide frequency range, from sub-harmonic frequencies to the Nyquist frequency. Although it is feasible to take the fundamental frequency measurement during power system operation, obtaining a wide-frequency impedance curve in real time is very challenging. This paper proposed an ANN-based approach to estimate wide-frequency system admittance of wind farms with power cables, through fundamental frequency measurements. Real-life uncertainties are considered, including shunt capacitor injection, filter inductance variance, cable aging, errors in voltage and current measurements, and the variance of other system parameters. The generalization ability of the ANN is validated on a new dataset with different uncertainty distributions, and the error sensitivity to the potential system parameter variance is evaluated. These results can be referenced in the data acquisition step in future neural-network-based applications.
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| 3. |
- Li, Yufei, et al.
(författare)
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Neural Network Models and Transfer Learning for Impedance Modeling of Grid-Tied Inverters
- 2022
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Ingår i: 2022 IEEE 13TH INTERNATIONAL SYMPOSIUM ON POWER ELECTRONICS FOR DISTRIBUTED GENERATION SYSTEMS (PEDG). - : Institute of Electrical and Electronics Engineers (IEEE).
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Konferensbidrag (refereegranskat)abstract
- The future power grid will be supported by a large number of grid-tied inverters whose dynamics are critical for grid stability and power flow control. The operating conditions of these inverters vary across a wide range, leading to different small-signal impedances and different grid-interface behaviors. Analytical impedance models derived at specific operating points can hardly capture nonlinearities and nonidealities of the physical systems. The applicability of electromagnetic transient (EMT) simulations is often limited by the system complexity and the available computational resources. This paper applies neural network and transfer learning to impedance modeling of gridtied inverters. It is shown that a neural network (NN) trained by data automatically acquired from EMT simulations outperforms the one trained by traditional analytical models when unknown information exist in simulations. Pre-training the NN with analytically calculated data can greatly reduce the amount of simulation data needed to achieve good modeling results.
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| 4. |
- Longhini, Alberta, et al.
(författare)
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Elastic Context : Encoding Elasticity for Data-driven Models of Textiles
- 2023
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Ingår i: Proceedings - ICRA 2023. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 1764-1770
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Konferensbidrag (refereegranskat)abstract
- Physical interaction with textiles, such as assistivedressing or household tasks, requires advanced dexterous skills.The complexity of textile behavior during stretching and pullingis influenced by the material properties of the yarn and bythe textile’s construction technique, which are often unknownin real-world settings. Moreover, identification of physicalproperties of textiles through sensing commonly available onrobotic platforms remains an open problem. To address this,we introduce Elastic Context (EC), a method to encode theelasticity of textiles using stress-strain curves adapted fromtextile engineering for robotic applications. We employ EC tolearn generalized elastic behaviors of textiles and examine theeffect of EC dimension on accurate force modeling of real-worldnon-linear elastic behaviors.
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