The past decade has seen an explosion of machine learning research and appli- cations; especially, deep learning methods have enabled key advances in many applicationdomains,suchas computervision,speechprocessing,andgameplaying. However, the performance of many machine learning methods is very sensitive to a plethora of design decisions, which constitutes a considerable barrier for new users. This is particularly true in the booming field of deep learning, where human engineers need to select the right neural architectures, training procedures, regularization methods, and hyperparameters of all of these components in order to make their networks do what they are supposed to do with sufficient performance. This process has to be repeated for every application. Even experts are often left with tedious episodes of trial and error until they identify a good set of choices for a particular dataset.
标签： Auto-Machine-Learning-Methods-Sys tems-Challenges
上传时间： 2020-06-10
上传用户：shancjb
The large-scale deployment of the smart grid (SG) paradigm could play a strategic role in supporting the evolution of conventional electrical grids toward active, flexible and self- healing web energy networks composed of distributed and cooperative energy resources. From a conceptual point of view, the SG is the convergence of information and operational technologies applied to the electric grid, providing sustainable options to customers and improved security. Advances in research on SGs could increase the efficiency of modern electrical power systems by: (i) supporting the massive penetration of small-scale distributed and dispersed generators; (ii) facilitating the integration of pervasive synchronized metering systems; (iii) improving the interaction and cooperation between the network components; and (iv) allowing the wider deployment of self-healing and proactive control/protection paradigms.
标签： Computational Intelligence
上传时间： 2020-06-10
上传用户：shancjb
This book is intended to be a general introduction to neural networks for those with a computer architecture, circuits, or systems background. In the introduction (Chapter 1), we define key vo- cabulary, recap the history and evolution of the techniques, and for make the case for additional hardware support in the field.
标签： Deep_Learning_for_Computer_Archit ects
上传时间： 2020-06-10
上传用户：shancjb
Computer science as an academic discipline began in the 1960’s. Emphasis was on programming languages, compilers, operating systems, and the mathematical theory that supported these areas. Courses in theoretical computer science covered finite automata, regular expressions, context-free languages, and computability. In the 1970’s, the study of algorithms was added as an important component of theory. The emphasis was on making computers useful. Today, a fundamental change is taking place and the focus is more on a wealth of applications. There are many reasons for this change. The merging of computing and communications has played an important role. The enhanced ability to observe, collect, and store data in the natural sciences, in commerce, and in other fields calls for a change in our understanding of data and how to handle it in the modern setting. The emergence of the web and social networks as central aspects of daily life presents both opportunities and challenges for theory.
标签： Foundations Science Data of
上传时间： 2020-06-10
上传用户：shancjb
General paradigm in solving a computer vision problem is to represent a raw image using a more informative vector called feature vector and train a classifier on top of feature vectors collected from training set. From classification perspective, there are several off-the-shelf methods such as gradient boosting, random forest and support vector machines that are able to accurately model nonlinear decision boundaries. Hence, solving a computer vision problem mainly depends on the feature extraction algorithm
标签： Convolutional Networks Neural Guide to
上传时间： 2020-06-10
上传用户：shancjb
空天地一体化通信综述，卫星、无人机、地面蜂窝系统协同网络
标签： Satellite-UAV-Vehicle Integrated Networks
上传时间： 2021-10-22
上传用户：yujinsong
5G中的SDN-NFV和云计算.pdf摘 要 通过介绍广义的SDN/NFV和云计算，结合未来5G网络的特点，分析了5G中上述技术的 应用前景和技术定位；结合5G的网络特点和现有网络的部署情况，总结了各技术间的逻辑关系以及运 营商的侧重点。引言 SDN/NFV 和云计算都是起源于 IT 领域的技术。 如今，云计算已经非常成熟，在 IT 领域已经大规模商 用，SDN技术作为新兴的转发技术，也已经被谷歌等互 联网巨头部署在多个数据中心。随着虚 拟化技术的发展，人们试图将更多的专有 设备虚拟化和软件化，从而达到降低成本 和灵活部署的目的，于是 NFV 的概念诞 生了。本文将结合广义上 3 种技术本身 的特点和未来5G的网络能力要求，分析 各技术在5G架构中的技术定位和前景， 同时结合实际的发展情况，总结未来运营 商在技术研发和业务模式上的侧重点。 1.1 广义的SDN及标准化进程 ONF 在 2012 年 4 月 发 布 白 皮 书 《Software- Defined Networking: The New Norm for Networks》
标签： 5G
上传时间： 2022-02-25
上传用户：jason_vip1
摘要：无线传感器网络（Wireless Sensor Networks，wSN是由许多具有低功率无线收发装置的传感器节点组成，它们监测采集周边环境信息并传送到基站进行处理在某一时刻通过wSN采集的数据量非常大，如何正确、高效地处理这些数据成为当前WSN研究中的一个热点。传感器节点一般部署在恶劣环境中，一些偶然因素会使采集的数据中出现不准确的数据，用户依据这样的数据很难准确判断出被测对象的真实状态。基于模糊理论的决策级数据融合算法能够很好的解决这个问题本文以国家863研究项目《基于无线传感器网络的铁路危险货物在途安全状态监测技术研究》为背景，结合铁路运输中棉花在途状态监测系统的开发，在分析了当前有效的决策级数据融合技术基础上，提出了基于模糊理论的决策级数据融合算法，该算法通过对采集数据进行处理和分析，以获得准确的被测对象状态的描述。本文的主要工作包括：（1）分析了WSN中传统的决策级数据融合算法，如自适应加权数据融合算法和算术平均数数据融合算法，总结这两种算法的优缺点和检测系统的需求，进步明确理想算法应达到的目标。（2）提出了基于模糊理论的两阶段数据融合算法：该算法第一阶段利用基于贴近度的数据融合算法进行同类数据的融合校准，这一阶段的目的是剔除错误的和可信度较差的数据，得到相对更加准确的数据，第二阶段利用模糊推理对第个阶段得到的异类数据进行融合推理，得到被测对象当前状态的描述，为决策提供支持（3）结合实测数据仿真本文所提出的算法，结果证明与传统的融合算法相比，可以更加准确的描述被测对象状态
标签： 无线传感器
上传时间： 2022-03-17
上传用户：
INTERNATIONAL STANDARD NORME INTERNATIONALEPart 4-2: Testing and measurement techniques – Electrostatic discharge immunity test.About the IEC The International Electrotechnical Commission (IEC) is the leading global organization that prepares and publishes International Standards for all electrical, electronic and related technologies.About IEC publications The technical content of IEC publications is kept under constant review by the IEC. Please make sure that you have the latest edition, a corrigenda or an amendment might have been published.
标签： IEC
上传时间： 2022-04-19
上传用户：fliang
说明： 基于51单片机的数字直流电压表相关材料，内容有原理图，仿真文件，论文材料，程序源码等。(The related materials of digital DC voltmeter based on 51 single chip computer include schematic simulation files, paper materials, program source code, etc.)
上传时间： 2022-05-16
上传用户：fliang
