ID 原文 译文
18665 理论和仿真分析发现，理想荷控(流控)忆阻器在直流和交流激励下，寄生电阻或寄生电容单独存在时不发生记忆衰退现象，但在寄生电阻和寄生电容同时存在的情况下会发生记忆衰退，其机理是寄生元件形成放电通路，从而导致荷控忆阻器产生了记忆衰退。 The oretical and simulation analysis shows that the idealcharge controlled (current controlled) memristor does not have fading memory when the parasitic resistance orcapacitance exists alone under the excitation of DC and AC, but fading memory occurs when the parasiticresistance and capacitance exist at the same time. The mechanism is that the parasitic elements form dischargepath, which leads to fading memory of the charge controlled memristor.
18666 针对现有群智感知平台在数据和酬金交付过程中存在的安全风险和隐私泄露问题，该文提出一种基于Tangle网络的分布式群智感知数据安全交付模型。 Considering the security risks and privacy leaks in the process of data and reward in the MobileCrowdSensing (MCS), a distributed security delivery model based on Tangle network is proposed.
18667 首先，在数据感知阶段，调用局部异常因子检测算法剔除异常数据，聚类获取感知数据并确定可信参与者节点。 Firstly, inthe data perception stage, the local outlier factor detection algorithm is used to eliminate the anomaly data,cluster the perception data and determine the trusted participant.
18668 然后，在交易写入阶段，使用马尔科夫蒙特卡洛算法选择交易并验证其合法性，通过注册认证中心登记完成匿名身份数据上传，并将交易同步写入分布式账本。 Then, in the transaction writing stage,Markov Monte Carlo algorithm is used to select the transaction and verify its legitimacy. The anonymousidentity data is uploaded by registering with the authentication center, and the transaction is synchronouslywritten to the distributed account book.
18669 最后，结合Tangle网络的累计权重共识机制，当交易安全性达到阈值时，任务发布者可进行数据和酬金的安全交付。 Finally, combined with Tangle network cumulative weight consensusmechanism, when the security of transaction reaches its threshold, task publishers can safely deliver data andrewards.
18670 仿真试验表明，在模型保护用户隐私的同时，增强了数据和酬金的安全交付能力，相比现有感知平台降低了时间复杂度和任务发布成本。 The simulation results show that the model not only protects user privacy, but also enhances theability of secure delivery of data and reward. Compared with the existing sensing platform, the model reducesthe time complexity and task publishing cost.
18671 RGB-D图像显著性检测是在一组成对的RGB和Depth图中识别出视觉上最显著突出的目标区域。 RGB-D saliency detection identifies the most visually attentive target areas in a pair of RGB andDepth images.
18672 已有的双流网络，同等对待多模态的RGB和Depth图像数据，在提取特征方面几乎一致。 Existing two-stream networks, which treat RGB and Depth data equally, are almost identical in feature extraction.
18673 然而，低层的Depth特征存在较大噪声，不能很好地表征图像特征。 As the lower layers Depth features with a lot of noise, it causes image features not be well characterized.
18674 因此，该文提出一种多模态特征融合监督的RGB-D图像显著性检测网络，通过两个独立流分别学习RGB和Depth数据，使用双流侧边监督模块分别获取网络各层基于RGB和Depth特征的显著图，然后采用多模态特征融合模块来融合后3层RGB和Depth高维信息生成高层显著预测结果。 Therefore, a multi-modal feature-fused supervision of RGB-D saliency detection network is proposed, RGB and Depth data are studied independently through two-stream , double-side supervision module is used respectively to obtain saliency maps of each layer, and then the multi-modal feature-fused module is used to later three layers of the fused RGB and Depth of higher dimensional information to generate saliency predicted results.