3D运动控制的一个飞跃

如果你认为专业工程和设计工具与使用者影响无关的话,那就再想想。出现在CAD、CAE核心包中的许多交互仿真和用户界面创新正是采纳游戏世界的线索。现在,输入设备都遵循相同的方向。Leap Motion,一家新的运动控制的公司,最新挑战将精确3D运动控制与传统计算机交互作用,无论执行基本的计算任务,如导航,还是做更多的创造性的工作,如精确的虚拟2D和3D图纸。在很大程度上与微软的Kinect Xbox插件风格相同,可以让人们通过手和全面的身体动作来玩游戏,Leap设备是通过USB端口附加到电脑,然后将它转变成一个动作识别设备。     

Incremental Learning Framework for Mining Big Data Stream

At this current time, data stream classification plays a key role in big data analytics due to its enormous growth. Most of the existing classification methods used ensemble learning, which is trustworthy but these methods are not effective to face the issues of learning from imbalanced big data, it also supposes that all data are pre-classified. Another weakness of current methods is that it takes a long evaluation time when the target data stream contains a high number of features. The main objective of this research is to develop a new method for incremental learning based on the proposed ant lion fuzzy-generative adversarial network model. The proposed model is implemented in spark architecture. For each data stream, the class output is computed at slave nodes by training a generative adversarial network with the back propagation error based on fuzzy bound computation. This method overcomes the limitations of existing methods as it can classify data streams that are slightly or completely unlabeled data and providing high scalability and efficiency. The results show that the proposed model outperforms state-of-the-art performance in terms of accuracy (0.861) precision (0.9328) and minimal MSE (0.0416).     

Deep Learning Enabled Object Detection and Tracking Model for Big Data Environment

Recently, big data becomes evitable due to massive increase in the generation of data in real time application. Presently, object detection and tracking applications becomes popular among research communities and finds useful in different applications namely vehicle navigation, augmented reality, surveillance, etc. This paper introduces an effective deep learning based object tracker using Automated Image Annotation with Inception v2 based Faster RCNN (AIA-IFRCNN) model in big data environment. The AIA-IFRCNN model annotates the images by Discriminative Correlation Filter (DCF) with Channel and Spatial Reliability tracker (CSR), named DCF-CSRT model. The AIA-IFRCNN technique employs Faster RCNN for object detection and tracking, which comprises region proposal network (RPN) and Fast R-CNN. In addition, inception v2 model is applied as a shared convolution neural network (CNN) to generate the feature map. Lastly, softmax layer is applied to perform classification task. The effectiveness of the AIA-IFRCNN method undergoes experimentation against a benchmark dataset and the results are assessed under diverse aspects with maximum detection accuracy of 97.77%.     

Analyzing Cross-domain Transportation Big Data of New York City with Semi-supervised and Active Learning

The majority of big data analytics applied to transportation datasets suffer from being too domain-specific, that is, they draw conclusions for a dataset based on analytics on the same dataset. This makes models trained from one domain (e.g. taxi data) applies badly to a different domain (e.g. Uber data). To achieve accurate analyses on a new domain, substantial amounts of data must be available, which limits practical applications. To remedy this, we propose to use semi-supervised and active learning of big data to accomplish the domain adaptation task: Selectively choosing a small amount of datapoints from a new domain while achieving comparable performances to using all the datapoints. We choose the New York City (NYC) transportation data of taxi and Uber as our dataset, simulating different domains with 90% as the source data domain for training and the remaining 10% as the target data domain for evaluation. We propose semi-supervised and active learning strategies and apply it to the source domain for selecting datapoints. Experimental results show that our adaptation achieves a comparable performance of using all datapoints while using only a fraction of them, substantially reducing the amount of data required. Our approach has two major advantages: It can make accurate analytics and predictions when big datasets are not available, and even if big datasets are available, our approach chooses the most informative datapoints out of the dataset, making the process much more efficient without having to process huge amounts of data.     

Data Analytics and Machine Learning for Smart Process Manufacturing: Recent Advances and Perspectives in the Big Data Era

Safe, efficient, and sustainable operations and control are primary objectives in industrial manufacturing processes. State-of-the-art technologies heavily rely on human intervention, thereby showing apparent limitations in practice. The burgeoning era of big data is influencing the process industries tremendously, providing unprecedented opportunities to achieve smart manufacturing. This kind of manufacturing requires machines to not only be capable of relieving humans from intensive physical work, but also be effective in taking on intellectual labor and even producing innovations on their own. To attain this goal, data analytics and machine learning are indispensable. In this paper, we review recent advances in data analytics and machine learning applied to the monitoring, control, and optimization of industrial processes, paying particular attention to the interpretability and functionality of machine learning models. By analyzing the gap between practical requirements and the current research status, promising future research directions are identified.     

基于小波分析的白车身阶跃测量数据处理

白车身焊装质量评价过程中，三坐标测量机(CMM)测量数据为非平稳随机过程。如何对白车身焊装质量做出准确评价是对其制造质量进行控制的基础。本文采用小波分析方法对白车身焊装过程中具有阶跃特征的CMM测量数据进行处理，使用处理后的数据对白车身焊装质量进行评价，并对数据处理过程中的问题进行了研究。通过对一个实际案例的应用分析，验证了小波分析方法对白车身阶跃测量数据处理的有效性。     

Virtual figure model crafting with VR HMD and Leap Motion

Handcrafting has been used for purposes ranging from learning and entertainment to stress relief. However, clay art and handicrafts require specialized space and physical materials. In this paper, we propose a virtual figure model crafting system that is developed to allow users to directly experience the figure model crafting process using a head-mounted, display-based, virtual reality environment. In the proposed system, users can freely generate the shape of a figure and decorate its exterior. In order to provide realistic crafting user experiences, we leverage motion grammar-based gestural input with Leap Motion and consider the support of multiple difficulty levels for user engagement. The preliminary user study results confirm that the proposed system provides immersive and interactive crafting experiences in a virtual environment.     

实现从制造大国到制造强国的跨越

为实现中国从"制造大国"到"制造强国"的转变,2013年中国工程院会同工业和信息化部联合组织开展"制造强国战略研究"重大咨询研究项目。本项目从机械、航空、航天、轨道、船舶、汽车、电力装备、信息电子、冶金、化工、纺织、家电、仪器十三个领域的制造业发展问题进行研究。研究分析了中国制造业所处现状、面临的机遇和挑战,总结中国制造业大而不强的原因,提出制造强国工程科技战略的思路和目标。研究建立了评价制造强国特征的指标体系,预测中国制造业发展趋势。项目提出了实现制造强国的指导方针和战略对策,明确跨入制造强国行列的"三步走"战略,为我国研究制订《中国制造2025》提供了科学支撑和决策支持。     

在未得到最终制冷剂前的替代工质

概述了制冷剂的排放或泄漏对大气的影响,评价了常用的两种过渡制冷剂HCFC123及HFC134a。     

基于改进蛙跳算法的图像自适应增强研究

为了提高图像增强的效果,提出改进蛙跳算法。首先对蛙群分组,一组采用群体智能算法,另一组用自适应算法;基于实数算法对图像像素映射编码;然后通过模糊集合对蛙跳微调更新;最后以均方误差函数作为评价函数,通过Beta非线性变换函数最优参数值实现图像自适应增强。实验仿真结果得出:改进蛙跳算法对图像增强对比度较高,处理时间少,像素数据求解精度高。     

Nonlithium Metal–Sulfur Batteries: Steps Toward a Leap

Present mobile devices, transportation tools, and renewable energy technologies are more dependent on newly developed battery chemistries than ever before. Intrinsic properties, such as safety, high energy density, and cheapness, are the main objectives of rechargeable batteries that have driven their overall technological progress over the past several decades. Unfortunately, it is extremely hard to achieve all these merits simultaneously at present. Alternatively, exploration of the most suitable batteries to meet the specific requirements of an individual application tends to be a more reasonable and easier choice now and in the near future. Based on this concept, here, a range of promising alternatives to lithium–sulfur batteries that are constructed with non‐Li metal anodes (e.g., Na, K, Mg, Ca, and Al) and sulfur cathodes are discussed. The systems governed by these new chemistries offer high versatility in meeting the specific requirements of various applications, which is directly linked with the broad choice in battery chemistries, materials, and systems. Herein, the operating principles, materials, and remaining issues for each targeted battery characteristics are comprehensively reviewed. By doing so, it is hoped that their design strategies are illustrated and light is shed on the future exploration of new metal–sulfur batteries and advanced materials.     

UNMASK，在我们失去创造力之前

《Do you know unmask？》——三个男孩刘展、匡峻、谭天帏的这件毕业作品成了他们日后团队名字的由来。作为中央美院雕塑系的学生，这件作品跳出了传统雕塑范畴，借用玩偶、平面、服装的表现形式展现一系列以自我形象为蓝本，凭借各自的形象特征为基础而设计出各自的虚拟形象。不仅帮助他们获得了当年毕业设计展的一等奖，更使他们日后的跨界创作顺理成章。除了雕塑，他们的作品涉及到了装置、服装、灯箱、玩具、平面等各种领域。而在艺术创作之外，他们更开始了以商业化的模式将自己的设计推向市场的努力。从小众创作到大众商业，或许这是又一个跨界。[编者按]