fluffy
发表于 2025-3-25 04:34:17
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简洁
发表于 2025-3-25 10:27:38
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overwrought
发表于 2025-3-25 12:00:11
https://doi.org/10.1057/9780230246744re not tampering. This paper, firstly introduces the basic principles and applications of blockchain, such as hash function, hash pointer, digital signature. and then implements a blockchain-based trusted log storage and verification system in MES system.
irradicable
发表于 2025-3-25 17:46:52
Improved Particle Swarm Algorithm and Its Application in Sensor Network Optimizationsing inertia weight and a contraction factor are used to enhance the PSO. The experimental results show that the improved PSO has faster convergence speed, and can effectively improve the signal coverage of the sensor network.
Diuretic
发表于 2025-3-25 20:45:44
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大方不好
发表于 2025-3-26 01:12:32
Manifold Learning Algorithm Based on Constrained Particle Swarm Multi-objective Optimization composed of multi-information objectives and multi-user requirements (constraints), and the design is based on the Lebesgue measure constraint processing technology particle swarm Manifold learning algorithm of multi-objective optimization algorithm to improve the calculation accuracy of popular learning algorithms.
AXIOM
发表于 2025-3-26 07:43:03
The Use of Trusted Log Storage and Verification System Based on Blockchain for Manufacturing Executire not tampering. This paper, firstly introduces the basic principles and applications of blockchain, such as hash function, hash pointer, digital signature. and then implements a blockchain-based trusted log storage and verification system in MES system.
生存环境
发表于 2025-3-26 10:47:31
Haunting and Nature: An Introduction, dataset of melanoma segmentation show that the proposed method obtains a series of network architectures with different sizes, and the obtained architectures achieve obvious improvements in term of both model sizes and prediction accuracies compared with several popular and manually designed variants of U-Net.
Flatter
发表于 2025-3-26 14:41:36
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充足
发表于 2025-3-26 18:08:19
https://doi.org/10.1007/978-3-319-98089-8tion with less loss and communication cost. The model is solved by many-objective evolutionary algorithm, and some solutions are selected as training parameters, and satisfactory results are obtained. The effectiveness of the proposed method is verified.