ID 原文 译文
40476 实验结果表明，该系统基于多传感器融合的步态检测技术，步态检测识别率平均达90.48%。 Experimental results show that the proposed system is based on the gait detection technology of multi-sensor fusion, with an average gait recognition rate of 90.48%.
40477 该系统便携性好、功耗低、测障效果好，在辅助行走领域具有一定的研究意义和实用价值。 The system has good portability, low power consumption, good obstacle detection effect, and has certain research significance and practical value in the field of auxiliary walking.
40478 针对蝴蝶优化算法存在收敛速度慢、寻优精度差和易陷入局部最优等缺陷，提出融合收敛因子和樽海鞘群的蝴蝶优化算法.受灰狼算法和樽海鞘群算法的启发分别将收敛因子融入全局位置和局部位置更新处，提高算法的寻优精度; Aiming at the shortcomings of the butterfly optimization algorithm, such as slow convergence speed, poor searching precision and easy to fall into local optimality. A butterfly optimization algorithm based on convergence factor and salp swarm is proposed, inspired by grey wolf algorithm and salp swarm algorithm, the convergence factor is integrated into global position and local position update respectively, the optimization precision of algorithm is improved;
40479 再结合樽海鞘群领导机制，平衡了算法的全局搜索和局部勘探能力. combined with the salp swarm leadership mechanism, the global search and local exploration capabilities of the algorithm are balanced.
40480 通过17个基准函数的测试，所有实验结果表明采用综合改进策略的算法在收敛速度、寻优精度和鲁棒性方面具有一定优势. By testing 17 benchmark functions, all the experimental results show that the algorithm using the comprehensive improved strategy has some advantages in terms of convergence speed, optimization accuracy and robustness.
40481 针对水下图像降质的问题，提出一种基于条件生成对抗网络(CGAN)的自适应密集特征融合水下图像增强算法。 Aiming at the problem that underwater image degradation. This paper proposed an adaptive dense feature fusion underwater image enhancement algorithm based on conditional generative adversarial network (CGAN).
40482 该算法提出一种新颖的自适应密集特征融合(ADFF)模块，通过自适应学习不同级别特征的空间重要性权重，从而促使网络从以前和现在的特征中学习更有效的特征进行融合。 The algorithm proposed a novel adaptive dense feature fusion (ADFF) module, which could prompt the network to learn more effective features from previous and current features for fusion by adaptively learning the spatial importance weights of different levels of features.
40483 实验中，采用U-Net结构的生成器，将ADFF模块集成在生成器的每一级别，使用WGAN-GP对抗损失与L_(1)和L_(2)损失的组合损失对网络模型进行约束。 In the experiment, the U-Net structure generator was used, the ADFF module was integrated at each level of the generator, and the WGAN-GP (Wasserstein GAN with gradient penalty) adversarial loss and combined loss of L1 and L2 loss was used to constrain the network model.
40484 实验结果表明，与其他水下图像增强算法进行对比，该算法在合成和真实数据集上均取得了更优越的性能，可以生成视觉效果更好的清晰水下图像。 Experimental results show that, compared with other underwater image enhancement algorithms, this algorithm achieves superior performance on both synthetic and real data sets, and can generate clear underwater images with better visual effects.
40485 在处理深度神经网络这类数据密集型应用的过程中，处理器和存储器间大量数据的频繁传输会造成严重的性能损耗和能量消耗，也是当前冯·诺伊曼架构最大的瓶颈.针对传统冯·诺伊曼体系架构的局限性，基于SRAM的存内计算技术将运算单元集成到内存中，支持数据的即存即算，彻底突破了冯·诺伊曼瓶颈，有望成为新一代智能计算架构. In the process of processing data-intensive applications such as deep neural networks, the frequent transfer of large amounts of data between the processor and the memory causes severe performance loss and energy consumption, which is the biggest bottleneck of the current von Neumann architecture. In view of the limitations of the traditional von Neumann architecture, the SRAM-based in-memory computing technology integrates the computing unit into the memory to support data storage and calculation, which completely breaks through the von Neumann bottleneck and is expected to become a new generation Intelligent computing architecture.