改进的kNN分类算法在工业物联网中的应用

在工业物联网中,k近邻分类(kNN)被广泛应用于缺陷产品识别和异常检测.但kNN自身存在计算复杂度高、不适用于分布式环境等缺点.因此,文中提出了一种安全有效的分布式kNN分类算法,以防止信息泄漏和控制流泄漏,同时支持分布式服务器上的大规模数据分类.首先设计了一个安全有效的向量同态加密方案.在该方案的基础上,提出了DkN...     

基于深度学习的SAR图像舰船目标检测综述

近年来,合成孔径雷达成像技术因具备全天时和全天候的目标感测能力,在海洋实时监测和管控等领域发挥着重要作用,特别是高分率SAR图像中的舰船目标检测成为当前的研究热点之一.首先分析基于深度学习的SAR图像舰船目标检测流程,并对样本训练数据集的构建、目标特征的提取和目标框选的设计等关键步骤进行归纳总结.然后对检测流程中的各部...     

面向B5G/6G的三三三网络体系架构和优化学习机制

面对未来B5G/6G网络是大连接复杂智能网络,极其大量的用户连接、需求连接、服务连接,加之3G、4G、5G甚至6G的综合运用,将导致网络变得极其复杂的挑战,提出了一种三三三网络体系架构,它是一个包含3类网络(核心网、接入网、终端网)、3种资源(频率带宽、功耗、时延)和3项需求(生活、工作、服务)的三维立体综合优化的体系...     

Improving Knowledge Graph Completion Using Soft Rules and Adversarial Learning

Knowledge graph is a useful resources and tools for describing entities and relationships in natural language processing tasks.However,the existing knowledge graph are incomplete.Therefore,knowledge graph completion technology has become a research hotspot in the field of artificial intelligence,but the traditional knowledge graph embedding method does not fully take into account the role of logic rules and the effect of false negative samples on knowledge embedding.Based on the logic rules of knowledge and the role of adversarial learning in knowledge embedding,we proposes a model to improve the completion of knowledge graph:soft Rules and graph adversarial learning (RUGA).Firstly,the traditional knowledge graph embedding model is trained as generator and discriminator by using adversarial learning method,and high-quality negative samples are obtained.Then these negative samples and the existing positive samples together constitute the label triple in the injection rule model.The whole model will benefit from both high-quality samples and logical rules.In addition,we evaluated the performance of link prediction task and triple classification task on Freebase and Yago datasets respectively.Finally,the experimental results show that the model can effectively improve the effect of knowledge graph completion.     

采用深度可分离卷积和改进特征融合的MSSD

单步多框检测器(Single Shot Multibox Detector,SSD)是一种优秀的目标检测模型,但是其对额外层的处理方式还需要进一步提升.因此,利用深度可分离卷积的思想设计新的深度可分离卷积模块改进模型中的额外层,采用紧邻特征图融合方法加强特征复用,综合设计了改进的目标检测模型(Modified SSD,...     

MLIMC: Machine Learning-Based Implicit-Solvent Monte Carlo

Monte Carlo (MC) methods are important computational tools for molecular structure optimizations and predictions. When solvent effects are explicitly considered, MC methods become very expensive due to the large degree of freedom associated with the water molecules and mobile ions. Alternatively implicit-solvent MC can largely reduce the computational cost by applying a mean field approximation to solvent effects and meanwhile maintains the atomic detail of the target molecule. The two most popular implicit-solvent models are the Poisson-Boltzmann (PB) model and the Generalized Born (GB) model in a way such that the GB model is an approximation to the PB model but is much faster in simulation time. In this work, we develop a machine learning-based implicit-solvent Monte Carlo (MLIMC) method by combining the advantages of both implicit solvent models in accuracy and efficiency. Specifically, the MLIMC method uses a fast and accurate PB-based machine learning (PBML) scheme to compute the electrostatic solvation free energy at each step. We validate our MLIMC method by using a benzene-water system and a protein-water system. We show that the proposed MLIMC method has great advantages in speed and accuracy for molecular structure optimization and prediction.     

基于拓扑优化和深度学习的新型结构生成方法

计算机辅助设计已广泛应用于结构计算和分析,但如何利用计算机智能生成最佳的新型结构还面临巨大挑战。针对这一问题,提出了一种基于拓扑优化和深度学习的新型结构智能生成方法。该方法首先通过结构拓扑优化分析获得不同参数下的优化结果制作训练集图片,并将训练集标签定义为相应的工况类型,然后应用最小二乘生成对抗网络(LSGAN)深度学习算法进行训练并生成大量的新型结构,最后建立评价指标和评估体系对生成的模型进行评价比较,根据需求选择最佳结构设计方案。结合一个铸钢支座节点底板设计的工程案例,详细阐述了上述方法的应用过程,并借助三维重构技术和增材制造技术实现结构模型的一体化制造。研究结果表明,基于拓扑优化和深度学习的新型结构智能生成方法不仅可以自动生成新的结构,而且可以进一步优化结构的材料用量和力学性能。     

Space-Air-Ground Integrated Mobile Crowdsensing for Partially Observable Data Collection by Multi-Scale Convolutional Graph Reinforcement Learning

Mobile crowdsensing (MCS) is attracting considerable attention in the past few years as a new paradigm for large-scale information sensing. Unmanned aerial vehicles (UAVs) have played a significant role in MCS tasks and served as crucial nodes in the newly-proposed space-air-ground integrated network (SAGIN). In this paper, we incorporate SAGIN into MCS task and present a Space-Air-Ground integrated Mobile CrowdSensing (SAG-MCS) problem. Based on multi-source observations from embedded sensors and satellites, an aerial UAV swarm is required to carry out energy-efficient data collection and recharging tasks. Up to date, few studies have explored such multi-task MCS problem with the cooperation of UAV swarm and satellites. To address this multi-agent problem, we propose a novel deep reinforcement learning (DRL) based method called Multi-Scale Soft Deep Recurrent Graph Network (ms-SDRGN). Our ms-SDRGN approach incorporates a multi-scale convolutional encoder to process multi-source raw observations for better feature exploitation. We also use a graph attention mechanism to model inter-UAV communications and aggregate extra neighboring information, and utilize a gated recurrent unit for long-term performance. In addition, a stochastic policy can be learned through a maximum-entropy method with an adjustable temperature parameter. Specifically, we design a heuristic reward function to encourage the agents to achieve global cooperation under partial observability. We train the model to convergence and conduct a series of case studies. Evaluation results show statistical significance and that ms-SDRGN outperforms three state-of-the-art DRL baselines in SAG-MCS. Compared with the best-performing baseline, ms-SDRGN improves 29.0% reward and 3.8% CFE score. We also investigate the scalability and robustness of ms-SDRGN towards DRL environments with diverse observation scales or demanding communication conditions.     

Evolution of Brains and Computers: The Roads Not Taken

When computers started to become a dominant part of technology around the 1950s, fundamental questions about reliable designs and robustness were of great relevance. Their development gave rise to the exploration of new questions, such as what made brains reliable (since neurons can die) and how computers could get inspiration from neural systems. In parallel, the first artificial neural networks came to life. Since then, the comparative view between brains and computers has been developed in new, sometimes unexpected directions. With the rise of deep learning and the development of connectomics, an evolutionary look at how both hardware and neural complexity have evolved or designed is required. In this paper, we argue that important similarities have resulted both from convergent evolution (the inevitable outcome of architectural constraints) and inspiration of hardware and software principles guided by toy pictures of neurobiology. Moreover, dissimilarities and gaps originate from the lack of major innovations that have paved the way to biological computing (including brains) that are completely absent within the artificial domain. As it occurs within synthetic biocomputation, we can also ask whether alternative minds can emerge from A.I. designs. Here, we take an evolutionary view of the problem and discuss the remarkable convergences between living and artificial designs and what are the pre-conditions to achieve artificial intelligence.