| 1. |
- Kristan, Matej, et al.
(författare)
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The Ninth Visual Object Tracking VOT2021 Challenge Results
- 2021
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Ingår i: 2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW 2021). - : IEEE COMPUTER SOC. - 9781665401913 ; , s. 2711-2738
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Konferensbidrag (refereegranskat)abstract
- The Visual Object Tracking challenge VOT2021 is the ninth annual tracker benchmarking activity organized by the VOT initiative. Results of 71 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in recent years. The VOT2021 challenge was composed of four sub-challenges focusing on different tracking domains: (i) VOT-ST2021 challenge focused on short-term tracking in RGB, (ii) VOT-RT2021 challenge focused on "real-time" short-term tracking in RGB, (iii) VOT-LT2021 focused on long-term tracking, namely coping with target disappearance and reappearance and (iv) VOT-RGBD2021 challenge focused on long-term tracking in RGB and depth imagery. The VOT-ST2021 dataset was refreshed, while VOT-RGBD2021 introduces a training dataset and sequestered dataset for winner identification. The source code for most of the trackers, the datasets, the evaluation kit and the results along with the source code for most trackers are publicly available at the challenge website(1).
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| 2. |
- Dai, Xiaoxia, et al.
(författare)
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Topology optimization of steady Navier-Stokes flow via a piecewise constant level set method
- 2018
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Ingår i: Structural and multidisciplinary optimization (Print). - : Springer. - 1615-147X .- 1615-1488. ; 57:6, s. 2193-2203
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a piecewise constant level set method for the topology optimization of steady Navier- Stokes flow. Combining piecewise constant level set functions and artificial friction force, the optimization problem is formulated and analyzed based on a design variable. The topology sensitivities are computed by the adjoint method based on Lagrangian multipliers. In the optimization procedure, the piecewise constant level set function is updated by a new descent method, without the needing to solve the Hamilton-Jacobi equation. To achieve optimization, the piecewise constant level set method does not track the boundaries between the different materials but instead through the regional division, which can easily create small holes without topological derivatives. Furthermore, we make some attempts to avoid updating the Lagrangian multipliers and to deal with the constraints easily. The algorithm is very simple to implement, and it is possible to obtain the optimal solution by iterating a few steps. Several numerical examples for both two- and three-dimensional problems are provided, to demonstrate the validity and efficiency of the proposed method.
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| 3. |
- Huang, Ze Hua, et al.
(författare)
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Utilizing Haematococcus pluvialis to simulate animal meat color in high-moisture meat analogues: Texture quality and color stability
- 2024
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Ingår i: Food Research International. - 0963-9969 .- 1873-7145. ; 175
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Tidskriftsartikel (refereegranskat)abstract
- The effect of Haematococcus pluvialis (HP) (0.25∼1.25 %) as a colorant during high moisture extrusion (50 %) on the texture and microstructural properties of soy protein-based high moisture meat analogs (HMMA) was evaluated. Furthermore, the stability of HP-induced meat like color of the HMMA as a function of light exposure, freeze/thawing, frozen storage and cooking temperature and duration was investigated. The addition of HP reduced the elasticity of HMMA but enhanced its hardness, chewiness, and resilience. HP addition at low levels promoted the flexible and disordered regions within the protein secondary structure while excessive HP addition was unfavorable for protein cross-linking. The optimal degree of texturization was achieved with 0.75 % HP. Sensory evaluations revealed that HMMA with 1 %HP had a color similar to fresh beef sirloin, while HMMA with 0.25 % HP had a color closer to fresh pork loin. Light exposure induced the greatest color loss of the meat analogs compared with the cooking and frozen storage. The a* value of HMMA containing 1.25 % HP decreased by 30 % during the 14 days of light exposure. Frozen storage at darkness efficiently preserved the meat-like color of the extrudates. Overall, HP was found as promising colorant for HMMA production but the storage condition of the extrudates should be carefully optimized.
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| 4. |
- Wang, Deyu, et al.
(författare)
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A Memristor-Based Learning Engine for Synaptic Trace-Based Online Learning
- 2023
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Ingår i: IEEE Transactions on Biomedical Circuits and Systems. - : Institute of Electrical and Electronics Engineers (IEEE). - 1932-4545 .- 1940-9990. ; 17:5, s. 1153-1165
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Tidskriftsartikel (refereegranskat)abstract
- The memristor has been extensively used to facilitate the synaptic online learning of brain-inspired spiking neural networks (SNNs). However, the current memristor-based work can not support the widely used yet sophisticated trace-based learning rules, including the trace-based Spike-Timing-Dependent Plasticity (STDP) and the Bayesian Confidence Propagation Neural Network (BCPNN) learning rules. This paper proposes a learning engine to implement trace-based online learning, consisting of memristor-based blocks and analog computing blocks. The memristor is used to mimic the synaptic trace dynamics by exploiting the nonlinear physical property of the device. The analog computing blocks are used for the addition, multiplication, logarithmic and integral operations. By organizing these building blocks, a reconfigurable learning engine is architected and realized to simulate the STDP and BCPNN online learning rules, using memristors and 180 nm analog CMOS technology. The results show that the proposed learning engine can achieve energy consumption of 10.61 pJ and 51.49 pJ per synaptic update for the STDP and BCPNN learning rules, respectively, with a 147.03× and 93.61× reduction compared to the 180 nm ASIC counterparts, and also a 9.39× and 5.63× reduction compared to the 40 nm ASIC counterparts. Compared with the state-of-the-art work of Loihi and eBrainII, the learning engine can reduce the energy per synaptic update by 11.31× and 13.13× for trace-based STDP and BCPNN learning rules, respectively.
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| 5. |
- Wang, Dong, et al.
(författare)
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Optimized fire resistance of alkali-activated high-performance concrete by steel fiber
- 2024
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Ingår i: Journal of thermal analysis and calorimetry (Print). - : Springer Nature. - 1388-6150 .- 1588-2926.
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Tidskriftsartikel (refereegranskat)abstract
- The behavior of alkali-activated ultra-high-performance concrete (A-UHPC) at elevated temperatures is unknown. This study addresses this gap by investigating the behavior of A-UHPC under varying temperatures with steel fiber additions (1%, 2%, and 3%), and considering target temperatures (20 °C, 200 °C, 400 °C, 600 °C, and 800 °C) as design variables. As the results, A-UHPC with steel fibers showed improved fire resistance, suffering less compressive strength loss at 800 °C than fiber-free A-UHPC. High temperatures initially optimized A-UHPC’s microstructure at 200 °C but later caused damage through microstructure propagation. Steel fibers enhanced A-UHPC’s ductility, resulting in ductile failure even at 800 °C. A-UHPC exhibited a unique mechanical degradation pattern under elevated temperatures, distinct from ordinary cement-based concrete. Empirical models accurately predicted its behavior, offering valuable insights for engineers dealing with heavy loads and high temperatures.
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