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Titlebook: Neural Information Processing; 28th International C Teddy Mantoro,Minho Lee,Achmad Nizar Hidayanto Conference proceedings 2021 Springer Nat

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樓主: Orthosis
41#
發(fā)表于 2025-3-28 18:00:37 | 只看該作者
Juan Gonger with the costs of machinery operations, became more expensive. Thus simpler alternatives to conventional plough tillage, termed minimum or conservation tillage, became attractive as offering savings in the cost of establishing crops [.]. A further incentive was the availability of herbicides for
42#
發(fā)表于 2025-3-28 21:03:37 | 只看該作者
A Novel Binary BCI Systems Based on?Non-oddball Auditory and?Visual Paradigms-oddball visual and auditory paradigms, respectively, which significantly outperformed the linear classifier model. These results open up novel avenues for practical ERP systems, which could increase the usability of current brain-computer interfaces remarkably.
43#
發(fā)表于 2025-3-29 00:28:51 | 只看該作者
A Just-In-Time Compilation Approach for?Neural Dynamics Simulationvides a friendly and highly flexible interface for users to define an arbitrary dynamical system, and the JIT compilation enables the defined model to run efficiently. We hope that BrainPy can serve as a general software for both research and education in computational neuroscience.
44#
發(fā)表于 2025-3-29 05:42:04 | 只看該作者
STCN-GR: Spatial-Temporal Convolutional Networks for?Surface-Electromyography-Based Gesture Recognitn STCN-GR to capture spatial-temporal information. Additionally, the connectivity of the graph can be adjusted adaptively in different layers of networks, which increases the flexibility of networks compared with the fixed graph structure used by original GCNs. On two high-density sEMG (HD-sEMG) dat
45#
發(fā)表于 2025-3-29 11:11:48 | 只看該作者
46#
發(fā)表于 2025-3-29 14:56:28 | 只看該作者
DFFCN: Dual Flow Fusion Convolutional Network for?Micro Expression Recognitionn datasets: CASME II, SAMM and SMIC with Leave-One-Subject-Out (LOSO) cross-validation. The results demonstrated that our method achieves competitive performance when compared with the existing approaches, with the best UF1 (0.8452) and UAR (0.8465).
47#
發(fā)表于 2025-3-29 16:18:03 | 只看該作者
48#
發(fā)表于 2025-3-29 23:06:10 | 只看該作者
Semantic Perception Swarm Policy with?Deep Reinforcement Learningention network is adopted to effectively model individual-level and group-level relational information. The distributed and transferable swarm policy can perceive the information of uncertain number of agents in swarm environments. Various simulations and real-world experiments on several challengin
49#
發(fā)表于 2025-3-30 00:43:43 | 只看該作者
Open-Set Recognition with?Dual Probability Learninga new method called Dual Probability Learning Model (DPLM). The model built a neural Gaussian Mixed Model for probability estimation. To learn this model, we also added the normalized joint probability of latent representations into the objective function in the training stage. The results showed th
50#
發(fā)表于 2025-3-30 04:11:12 | 只看該作者
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