6000m深海扬矿系统仿真分析

深海多金属结核开采系统的扬矿系统主要由扬矿硬管、扬矿泵、中间仓、扬矿软管等组成的长管线系统构成,其水下的运动学行为,决定了深海采矿的成败.运用几何非线性有限元理论对6000m深海采矿系统的扬矿系统进行了力学分析;在扬矿系统受管道及管内流体重力,海水浮力以及水平液动力作用的情况下,用有限元方法分别对几种工况下的扬矿系统进行了仿真计算,其结果可作为扬矿系统的设计和操作控制的依据.     

基于返回结果的DeepWeb查询接口识别

互联网上存在许多有价值的信息,搜索引擎只能索引静态页面,无法索引DeepWeb数据,而Deep Web通常以表单形式存在,只有提交表单查询才能获得其数据,如何发现和识别Deep web查询接口成为人们关注的问题.在分析表单表现形式与功能内在的联系的基础上,提出一个表单的抽象模型,依此过滤非Deep Web查询接口的表单.通过对返回结果页面分析方法,实现Deep W出查询接口的识别,实验结果证明了该方法的有效性.     

Deep Web数据集成中查询处理的研究与进展

随着Web上在线数据库的大量涌现,Deep Web数据集成成为当前信息领域的一个研究热点,而查询处理是其中的一个重要的组成部分。由于Web数据库具有规模大、自治性、异构性以及动态性等特点,使得Deep Web数据集成中的查询处理比传统的分布环境下的查询处理更具挑战性。围绕Deep Web数据集成中查询处理的三个关键研究点:模式匹配、Web数据库的选择以及查询转换,综述了近年来国际上相关的、具代表性的研究成果,分析了这些方法的优缺点,总结并展望了未来的发展方向。     

书籍搜索领域Deep Web数据集成系统

随着在线数据库的迅速增长,可以访问的数据库资源大大增多,但它们的信息传统搜索引擎无法获得,它隐藏在网站背后,成为人们快速有效获取信息的障碍。为了获得Deepweb中大量有价值的隐藏信息,需要整合各在线异构数据源,以便在同一领域内比较某一事物的大量相关信息。目前,越来越多的人采取网上买书的消费方式,针对这个消费热点问题,设计了一个书籍搜索领域的Deep Web数据集成系统,提供一个集成的查询接口,使得用户可以方便地进行查找和比对。     

Jointly improving energy efficiency and smoothing power oscillations of integrated offshore wind and photovoltaic power: a deep reinforcement learning approach

This paper proposes a novel deep reinforcement learning (DRL) control strategy for an integrated ofshore wind and photovoltaic (PV) power system for improving power generation efciency while simultaneously damping oscillations. A variable-speed ofshore wind turbine (OWT) with electrical torque control is used in the integrated ofshore power system whose dynamic models are detailed. By considering the control system as a partially-observable Markov decision process, an actor-critic architecture model-free DRL algorithm, namely, deep deterministic policy gradient, is adopted and implemented to explore and learn the optimal multi-objective control policy. The potential and efectiveness of the integrated power system are evaluated. The results imply that an OWT can respond quickly to sudden changes of the infow wind conditions to maximize total power generation. Signifcant oscillations in the overall power output can also be well suppressed by regulating the generator torque, which further indicates that complementary operation of ofshore wind and PV power can be achieved.     

Deep learning-based 3D point cloud classification: A systematic survey and outlook

In recent years, point cloud representation has become one of the research hotspots in the field of computer vision, and has been widely used in many fields, such as autonomous driving, virtual reality, robotics, etc. Although deep learning techniques have achieved great success in processing regular structured 2D grid image data, there are still great challenges in processing irregular, unstructured point cloud data. Point cloud classification is the basis of point cloud analysis, and many deep learning-based methods have been widely used in this task. Therefore, the purpose of this paper is to provide researchers in this field with the latest research progress and future trends. First, we introduce point cloud acquisition, characteristics, and challenges. Second, we review 3D data representations, storage formats, and commonly used datasets for point cloud classification. We then summarize deep learning-based methods for point cloud classification and complement recent research work. Next, we compare and analyze the performance of the main methods. Finally, we discuss some challenges and future directions for point cloud classification.     

AR-based deep learning for real-time inspection of cable brackets in aircraft

In the process of aircraft assembly, there exist numerous and ubiquitous cable brackets that shall be installed on frames and subsequently need to be manually verified with CAD models. Such a task is usually performed by special operators, hence is time-consuming, labor-intensive, and error-prone. In order to save the inspection time and increase the reliability of results, many researchers attempt to develop intelligent inspection systems using robotic, AR, or AI technologies. However, there is no comprehensive method to achieve enough portability, intelligence, efficiency, and accuracy while providing intuitive task assistance for inspectors in real time. In this paper, a combined AR+AI system is introduced to assist brackets inspection in a more intelligent yet efficient manner. Especially, AR-based Mask R-CNN is proposed by skillfully integrating markerless AR into deep learning-based instance segmentation to generate more accurate and fewer region proposals, and thus alleviates the computation load of the deep learning program. Based on this, brackets segmentation can be performed robustly and efficiently on mobile devices such as smartphones or tablets. By using the proposed system, CAD model checking can be automatically performed between the segmented physical brackets and the corresponding virtual brackets rendered by AR in real time. Furthermore, the inspection results can be directly projected on the corresponding physical brackets for the convenience of maintenance. To verify the feasibility of the proposed method, experiments are carried out on a full-scale mock-up of C919 aircraft main landing gear cabin. The experimental results indicate that the inspection accuracy is up to 97.1%. Finally, the system has been deployed in the real C919 aircraft final-assembly workshop. The preliminary evaluation reveals that the proposed real-time AR-assisted intelligent inspection approach is effective and promising for large-scale industrial applications.     

A survey on privacy inference attacks and defenses in cloud-based Deep Neural Network

Deep Neural Network (DNN), one of the most powerful machine learning algorithms, is increasingly leveraged to overcome the bottleneck of effectively exploring and analyzing massive data to boost advanced scientific development. It is not a surprise that cloud computing providers offer the cloud-based DNN as an out-of-the-box service. Though there are some benefits from the cloud-based DNN, the interaction mechanism among two or multiple entities in the cloud inevitably induces new privacy risks. This survey presents the most recent findings of privacy attacks and defenses appeared in cloud-based neural network services. We systematically and thoroughly review privacy attacks and defenses in the pipeline of cloud-based DNN service, i.e., data manipulation, training, and prediction. In particular, a new theory, called cloud-based ML privacy game, is extracted from the recently published literature to provide a deep understanding of state-of-the-art research. Finally, the challenges and future work are presented to help researchers to continue to push forward the competitions between privacy attackers and defenders.     

基于深度神经网络和联邦学习的网络入侵检测

在高速网络环境中,对复杂多样的网络入侵进行快速准确的检测成为目前亟待解决的问题。联邦学习作为一种新兴技术,在缩短入侵检测时间与提高数据安全性上取得了很好的效果,同时深度神经网络（DNN）在处理海量数据时具有较好的并行计算能力。结合联邦学习框架并将基于自动编码器优化的DNN作为通用模型,建立一种网络入侵检测模型DFC-NID。对初始数据进行符号数据预处理与归一化处理,使用自动编码器技术对DNN实现特征降维,以得到DNN通用模型模块。利用联邦学习特性使得多个参与方使用通用模型参与训练,训练完成后将参数上传至中心服务器并不断迭代更新通用模型,通过Softmax分类器得到最终的分类预测结果。实验结果表明,DFC-NID模型在NSL-KDD与KDDCup99数据集上的准确率平均达到94.1%,与决策树、随机森林等常用入侵检测模型相比,准确率平均提升3.1%,在攻击类DoS与Probe上,DFC-NID的准确率分别达到99.8%与98.7%。此外,相较不使用联邦学习的NO-FC模型,DFC-NID减少了83.9%的训练时间。     

Deep ensemble-based hard sample mining for food recognition

Deep neural networks represent a compelling technique to tackle complex real-world problems, but are over-parameterized and often suffer from over- or under-confident estimates. Deep ensembles have shown better parameter estimations and often provide reliable uncertainty estimates that contribute to the robustness of the results. In this work, we propose a new metric to identify samples that are hard to classify. Our metric is defined as coincidence score for deep ensembles which measures the agreement of its individual models. The main hypothesis we rely on is that deep learning algorithms learn the low-loss samples better compared to large-loss samples. In order to compensate for this, we use controlled over-sampling on the identified ”hard” samples using proper data augmentation schemes to enable the models to learn those samples better. We validate the proposed metric using two public food datasets on different backbone architectures and show the improvements compared to the conventional deep neural network training using different performance metrics.