RTX技术文章V1.3

Application NoteTable of Contents Chapter 1. NVIDIA Hardware Video Encoder (1)1.1. Introduction (1)1.2. NVENC Capabilities (1)1.3. NVENC Licensing Policy (4)1.4. NVENC Performance (4)1.5. Programming NVENC (5)1.6. FFmpeg Support (6)Chapter 1.NVIDIA Hardware VideoEncoder1.1.IntroductionNVIDIA GPUs - beginning with the Kepler generation - contain a hardware-based encoder (referred to as NVENC in this document) which provides fully accelerated hardware-based video encoding and is independent of graphics/CUDA cores. With end-to-end encoding offloaded to NVENC, the graphics/CUDA cores and the CPU cores are free for other operations. For example, in a game recording scenario, offloading the encoding to NVENC makes the graphics engine fully available for game rendering. In the video transcoding use-case, video encoding/decoding can happen on NVENC/NVDEC in parallel with other video post-/pre-processing on CUDA cores. The hardware capabilities available in NVENC are exposed through APIs referred to as NVENCODE APIs in the document. This document provides information about the capabilities of the hardware encoder and features exposed through NVENCODE APIs.1.2.NVENC CapabilitiesNVENC can perform end-to-end encoding for H.264, HEVC 8-bit, HEVC 10-bit, AV1 8-bit and AV1 10-bit. This includes motion estimation and mode decision, motion compensation and residual coding, and entropy coding. It can also be used to generate motion vectors between two frames, which are useful for applications such as depth estimation, frame interpolation, encoding using other codecs not supported by NVENC, or hybrid encoding wherein motion estimation is performed by NVENC and the rest of the encoding is handled elsewhere in the system. These operations are hardware accelerated by a dedicated block on GPU silicon die. NVENCODE APIs provide the necessary knobs to utilize the hardware encoding capabilities. Table 1 summarizes the capabilities of the NVENC hardware exposed through NVENCODE APIs and Table 2 lists the features exposed in Video Codec SDK 12.0.Table 1.NVENC Hardware Capabilities‣Y : Supported, N : Not supported‣*Supported in select Pascal generation GPUs Table 2.What’s new in Video Codec SDK 12.01.3.NVENC Licensing PolicyThere is no change in licensing policy in the current SDK in comparison to the previous SDK. The licensing policy is as follows:As far as NVENC hardware encoding is concerned, NVIDIA GPUs are classified into two categories: “qualified” and “non-qualified”. On qualified GPUs, the number of concurrent encode sessions is limited by available system resources (encoder capacity, system memory, video memory etc.). On non-qualified GPUs, the number of concurrent encode sessions is limited to 3 per system. This limit of 3 concurrent sessions per system applies to the combined number of encoding sessions executed on all non-qualified cards present in the system.For a complete list of qualified and non-qualified GPUs, refer to https:/// nvidia-video-codec-sdk..For example, on a system with one Quadro RTX4000 card (which is a qualified GPU) and three GeForce cards (which are non-qualified GPUs), the application can run N simultaneous encode sessions on Quadro RTX4000 card (where N is defined by the encoder/memory/hardware limitations) and 3 sessions on all the GeForce cards combined. Thus, the limit on the number of simultaneous encode sessions for such a system is N + 3.1.4.NVENC PerformanceWith every generation of NVIDIA GPUs (Maxwell 1st/2nd gen, Pascal, Volta, Turing, Ampere and Ada), NVENC performance has increased steadily. Table 3provides indicative1NVENC performance on Pascal, Turing, and Ada GPUs for different presets and rate control modes (these two factors play a major role in determining the performance and quality). Note that performance numbers in Table 3 are measured on GeForce hardware with assumptions listed under the table. The performance varies across GPU classes (e.g. Quadro, Tesla), and scales (almost) linearly with the clock speeds for each hardware.While first-generation Maxwell GPUs had one NVENC engine per chip, certain variants of the second-generation Maxwell, Pascal, Volta and Ada GPUs have two/three NVENC engines per chip. This increases the aggregate encoder performance of the GPU. NVIDIA driver takes care of load balancing among multiple NVENC engines on the chip, so that applications don’t require any special code to take advantage of multiple encoders and automatically benefit from higher encoder capacity on higher-end GPU hardware. The encode performance listed in Table 3 is given per NVENC engine. Thus, if the GPU has 2 NVENCs (e.g. GP104, AD104), multiply the corresponding number in Table 3 by the number of NVENCs per chip to get aggregate maximum performance (applicable only when running multiple simultaneous encode sessions). Note that unless Split Frame Encoding is enabled, performance with single encoding session cannot exceed performance per NVENC, regardless of the number of NVENCs present on the GPU. Multi NVENC Split Frame Encoding is a feature introduced in SDK12.0 on Ada GPUs for HEVC and AV1. Refer to the NVENC Video Encoder API Programming Guide for more details on this feature.1Encoder performance depends on many factors, including but not limited to: Encoder settings, GPU clocks, GPU type, video content type etc.NVENC hardware natively supports multiple hardware encoding contexts with negligible context-switching penalty. As a result, subject to the hardware performance limit and available memory, an application can encode multiple videos simultaneously. NVENCODE API exposes several presets, rate control modes and other parameters for programming the hardware. A combination of these parameters enables video encoding at varying quality and performance levels. In general, one can trade performance for quality and vice versa.Table 3.NVENC encoding performance in frames/second (fps)‣Resolution/Input Format/Bit depth: 1920 × 1080/YUV 4:2:0/8-bit‣Above measurements are made using the following GPUs: GTX 1060 for Pascal, RTX 8000 for Turing, RTX 3090 for Ampere, and RTX 4090 for Ada. All measurements are done at the highest video clocks as reported by nvidia-smi (i.e. 1708 MHz, 1950 MHz, 1950 MHz, 2415 MHz for GTX 1060, RTX 8000, RTX 3090, and RTX 4090 respectively). The performance should scale according to the video clocks as reported by nvidia-smi for other GPUs of every individual family. Information on nvidia-smi can be found at https:/// nvidia-system-management-interface.‣H.264 and HEVC encoding fps for Volta GPU can be obtained by multiplying the Pascal fps in the above table by ratio of the clocks, as reported by nvidia-smi.‣Software: Windows 11, Video Codec SDK 12.0, NVIDIA display driver: 522.25‣CBR: Constant bitrate rate control mode, VBR: Variable bitrate rate control mode, LL : Low latency tuning info, HQ: High quality tuning info1.5.Programming NVENCRefer to the SDK release notes for information regarding the required driver version.Refer to the documents and the sample applications included in the SDK package for details on how to program NVENC.1.6.FFmpeg SupportFFmpeg is the most popular multimedia transcoding tool used extensively for video and audio transcoding.The video hardware accelerators in NVIDIA GPUs can be effectively used with FFmpeg to significantly speed up the video decoding, encoding and end-to-end transcoding at very high performance. For more information on how to use NVENC or NVDEC with FFmpeg, please refer to the FFmpeg guide in the Video Codec SDK.Note that FFmpeg is open-source project and its usage is governed by specific licenses and terms and conditions for FFmpeg.NoticeThis document is provided for information purposes only and shall not be regarded as a warranty of a certain functionality, condition, or quality of a product. NVIDIA Corporation (“NVIDIA”) makes no representations or warranties, expressed or implied, as to the accuracy or completeness of the information contained in this document and assumes no responsibility for any errors contained herein. NVIDIA shall have no liability for the consequences or use of such information or for any infringement of patents or other rights of third parties that may result from its use. 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No contractual obligations are formed either directly or indirectly by this document.NVIDIA products are not designed, authorized, or warranted to be suitable for use in medical, military, aircraft, space, or life support equipment, nor in applications where failure or malfunction of the NVIDIA product can reasonably be expected to result in personal injury, death, or property or environmental damage. NVIDIA accepts no liability for inclusion and/or use of NVIDIA products in such equipment or applications and therefore such inclusion and/or use is at customer’s own risk. NVIDIA makes no representation or warranty that products based on this document will be suitable for any specified use. Testing of all parameters of each product is not necessarily performed by NVIDIA. It is customer’s sole responsibility to evaluate and determine the applicability of any information contained in this document, ensure the product is suitable and fit for the application planned by customer, and perform the necessary testing for the application in order to avoid a default of the application or the product. Weaknesses in customer’s product designs may affect the quality and reliability of the NVIDIA product and may result in additional or different conditions and/or requirements beyond those contained in this document. 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Science and Technology & Innovation｜科技与创新2024年第04期DOI：10.15913/ki.kjycx.2024.04.009基于注意力机制和动态卷积的滚珠螺杆表面缺陷识别符诗语1，高齐2（1.江苏理工学院汽车与交通工程学院，江苏常州213001；2.江苏理工学院机械工程学院，江苏常州213001）摘要：针对滚珠螺杆表面缺陷识别特征信息提取困难和识别精度较低等问题，提出了一种基于注意力机制和动态卷积的滚珠螺杆表面缺陷识别方法。

首先，利用AlexNet网络进行缺陷样本图像的特征提取；然后，引入卷积注意力机制模块，在通道维度和空间维度增强缺陷区域位置权重，有效抑制相似背景干扰；最后，采用动态卷积模块对不同尺度的缺陷特征图像进行融合，提升模型特征提取能力，捕获丰富上下文信息。

实验表明，该方法在滚珠螺杆表面缺陷测试集上精确率为96.6%，召回率为96.5%，具有良好的实际工业应用价值。

关键词：缺陷识别；AlexNet；注意力机制；动态卷积中图分类号：TP183 文献标志码：A 文章编号：2095-6835（2024）04-0039-03滚珠螺杆作为转换机械运动的精密传动组件[1]，其质量将会直接影响仪器性能。

但在加工制造或运行时，受不利条件影响，滚动螺杆易出现“点蚀”现象[2]，最终导致机械设备损坏。

因此，实现高效的滚珠螺杆表面缺陷识别，对于提高工业产能和生产制造水平具有重要意义[3]。

近年来，随着深度学习模型的发展，诸多计算机视觉领域应用卷积神经网络解决了各类挑战，例如人脸识别、自然语言处理和目标检测等，因此，许多学者尝试将深度学习方法应用于表面缺陷检测。

文献[4]设计了一种新颖的级联式自动编码器结构，实现了金属表面缺陷有效识别，相比于传统视觉方法，实时性和精确率都取得了较大的提升，但当缺陷目标较小时，模型易受干扰，导致表现一般。

文献[5]利用迁移学习和特征重用技术，提出了一种基于DenseNet121网络的齿轮表面缺陷识别方法，该算法综合性能优异，但针对划痕和无缺陷2类相似图片的识别效果有待提升。

Date Version Authors Description 2022.10.27V1.0Leon Wang 概述2022.11.28V1.5Leon Wang 安装部署vGPU 配置安装手册vWS 14.3+vSPhere 7.0.3+vCenter 7.0.3修订记录1. NVIDIA vGPU 概述1.1 什么是NVIDIA vGPUNVIDIA 虚拟 GPU (vGPU) 软件为众多工作负载（从图形丰富的虚拟工作站到数据科学和 AI ）提供强大的 GPU 性能，使 IT 能够利用虚拟化的管理和安全优势以及现代工作负载所需的 NVIDIA GPU 的性能。

NVIDIA vGPU 软件安装在云或企业数据中心服务器的物理 GPU 上，会创建虚拟 GPU ，这些 GPU 可以在多个虚拟机（可随时随地通过任意设备访问）之间共享。

1.2 NVIDIA vGPU 软件产品分类1.2.1 NVIDIA 虚拟计算服务器 (vCS)仅支持CUDA ，Guest OS 仅支持Linux OS 。

加速基于 KVM 的基础架构上的虚拟化 AI 计算工作负载。

若是基于VMWare VSPhere 的基础架构，请查阅NVIDIA AI Enterprise 软件套件。

AI 、深度学习和数据科学工作流程需要出色的计算能力。

借助新款 NVIDIA 数据中心 GPU （包括 NVIDIA A30 Tensor Core GPU ），NVIDIA 虚拟计算服务器 (vCS) 可助力数据中心加速服务器虚拟化，以便可以在由 NVIDIA vGPU 技术驱动的虚拟机 (VM) 中运行计算密集程度极高的工作负载（例如人工智能、深度学习和数据科学）。

1.2.2 NVIDIA RTX 虚拟工作站 (vWS)支持DiriectX，OpenGL，Vulkan等图形API，同时支持CUDA/OpenCL计算API，适用于使用图形应用程序的创意和技术专业人士的虚拟工作站。

多合一驱动器规格说明书匯出日期：2023-05-10修改日期：2023-01-31•••••••••• 1.2.3.4.5.6.7.8.9.10.11.12.13.1.2.型号定义(Definition of Multi-axis Servo Drive)规格(Specification)通用规格(General Spec.)一般规格(Spec.)外型尺寸(External Dimensions)硬体安装与配线(Installation and Wiring Instructions)// 通用规格(General Spec.)// 一般规格(Spec.)// 外型尺寸(External Dimensions)// 硬体安装与配线(Installation and Wiring Instructions)版本号(CF 版本)编修日期编修内容Excel 档作者审查核准v.442022-6-27將所有表格置換成CF 格式新增S08-SMD34E2550-00新增S08-SMD34E2550-04新增S08-SMD34E3575-00新增S08-SMD34E3575-04新增S08-SMD34E50150-00新增S08-SMD34E50150-04新增S08-SMD33C2575-00新增S08-SMD33C35100-00新增S08-SMD22C20-00新增S08-SMD23C20-00新增S08-SMD24C20-00將部分不具有鋰電池盒的上代驅動器移至下架區--鄧乃維v1.82022-5-19新增SMH-20/20-50-M 三合一驅動器修正驅動器重量业务选型-多合一驱动器_v1.8.xlsx 劉任翔文件目录文件履历文件目录文件履历1.2.1.2.版本号(CF 版本)编修日期编修内容Excel 档作者审查核准v1.72020-11-20新增34D 五回授口版本驅動器將34C 移至接單生產區业务选型-多合一驱动器_v1.7.xlsx 黃揚程v1.62020/04/24改版大四合一34B 成34C 新增大四合一34D 业务选型-多合一驱动器_v1.6.xlsx 李宇雙v1.52020/01/02導入SAP 料號业务选型-多合一驱动器_v1.5.xlsx 李宇雙v1.42019/12/30更新規格，修改回生電阻；新增最大搭配規格业务选型-多合一驱动器_v1.4.xlsx 李宇雙v1.32019/11/13修改功率、額定、尖峰電流业务选型-多合一驱动器_v1.3.xlsx 李宇雙v1.22019/01/23新增品名敘述业务选型-多合一驱动器_v1.2.xlsx 楊娟v1.12018/12/0533B 及34B 上線，修改驅動器規格與外型尺寸业务选型-多合一驱动器_v1.1.xlsx 楊娟V1.02018/09/19新增新代多合一驱动器之规格与外型尺寸、建议搭配扩充卡/扩充模组等选型资讯业务选型-多合一驱动器_v1.0.xlsx刘思伶1.••1 型号定义(Definition of Multi-axis Servo Drive)　1st 2nd 3rd 4th 項目Item SMD50/50/50150XL定義Definition系列號 SeriesSMD series （220V ）SMH series （380V ）各軸向模塊電流 [A] Axial Module Current 50：模塊電流Module Current 50 A主軸模塊電流[A]Axial Module Current 150：模塊電流Module Current 150 A外型尺寸 Dimension2.a.i.ii.iii.iv.1.2.v.vi.b.2 规格(Specification)2.1 通用规格(General Spec.)控制方式 Control Method: 3-phase full wave rectification, SVPWM －VVVF control PC 通讯介面 PC Interface : USB控制器串列通讯介面 Controller Serial Communication : M3输入输出讯号 I/O Signal :数位输入Digital Input: 4 ports 数位输出Digital Output: 2 ports冷却方式 Cooling Method : 风扇冷却 Fan cooling 鋰電池 Battery: 標配 Y2.2 一般规格(Spec.)註：*最大搭配規格： 超過本表最大搭配軸向電機規格，請選配外置回生電阻。

RTX2009正式版技术白皮书腾讯科技（深圳）有限公司2010年10月RTX2010技术白皮书目录第一章系统概述 (3)第二章体系架构 (4)2.1系统整体架构 (4)2.2软硬件部署 (6)第三章技术特点 (8)3.1先进合理的企业应用理念 (8)3.1.1组织架构的自由定义 (8)3.2高效率的通信服务 (9)3.2.1高效的数据传输机制 (9)3.2.2先进的多媒体技术应用 (9)3.2.3稳定高效的数据/文件存储 (10)3.3 远程登录支持 (11)3.3.1 远程登录的管理 (11)3.4安全的企业系统服务 (11)3.4.1数据加密打包传输（128位标准对称加密算法） (11)3.4.2严谨的用户权限机制 (12)3.4.3签名和令牌 (12)3.5 RTX的可扩展性 (14)3.5.1为用户提供二次开发接口（API） (14)3.5.2 高度OA应用集成 (14)3.5.3 灵活强大的应用，服务扩展体系 (15)第四章技术参数 (17)4.1客户端 (17)4.2服务器端 (17)4.3操作要求 (18)第一章系统概述随着互联网的普及发展，即时通信（Instant Messaging）已经成为人们交流的重要手段。

在企业中，目前交流信息的手段除了传统的会议、电话、纸质文档之外，文件共享、电子邮件是比较常见的方式，即时通信作为一种更加便捷、更加有效的交流方式，在企业中也得到了广泛的应用，许多企业的员工都在非正式的使用QQ、MSN等即时通信软件。

在这方面，很多企业需要一个专门面向商业应用的即时通信软件，来加强企业内部、企业与客户之间的信息交流。

腾讯公司作为国内及亚洲最大的即时通信供应商，利用在个人即时通信市场积累的产品开发经验及市场运营经验，为国内广大的企业用户提供适合中国国情的商用即时通信软件。

原有的QQ品牌，带有娱乐交友色彩，并不太适用于企业市场；针对商务人士和企业用户的需要，腾讯推出了RTX（腾讯通）这个新的产品品牌，以更好的服务广大企业用户。

CMNET城域数据网技术方案--业务接入、VLAN规划、路由方案（v1.3）中国移动通信集团山东有限公司二零一零年六月文档修改历史目录1 城域数据网概述 (4)1.1网络定位 (4)1.2业务定位 (4)1.3分流与实现 (4)1.4QoS部署方案 (5)1.5城域网组网示意图 (5)2 省网、城域网路由协议部署 (6)2.1IGP协议规划 (7)2.2OSPF协议规划： (8)2.3Metric规划： (10)2.4BGP协议规划： (11)3 城域数据网组织层次以及设备命名规范 (13)4 IP地址规划与分配原则 (15)5 CMNET与GPRS、IMS、PON、PTN、业务系统互联组网 (17)5.1CMNET与GRPS互联 (17)5.2CMNET与IMS(核心网、SBC)互联组网 (17)5.3CMNET与PON互联组网 (17)5.4CMNET与PTN/IP RAN互联 (18)5.5CMNET与业务系统组网 (19)6 VPN (20)7 VLAN规划 (20)8 QoS (22)9 WLAN核心网与城域数据网融合方案 (22)10 城域网VRRP规划 (23)11 城域网防火墙NAT部署方案 (23)12 个人有线宽带业务到铁通组网 (25)附1:全网视图 (28)附2:软交换关口局信令CMNET备份通道 (29)附3 省网路由器ISIS NET与is-name编号 (29)附4 路由缺省优先级 (29)附5 CMNET专线私网地址接入原则与要求 (30)附6:用户接入侧组网分析 (31)1 城域数据网概述1.1网络定位CMNet城域网界于CMNet骨干网/省网与城域接入网之间，提供多种业务在城域内的互联及CMNet骨干网/省网的接入，并且保证各种业务的安全性和服务质量。

CMNet城域网是CMNet省网在城域内的延伸，通过核心路由器上连至CMNet省网。

现阶段，CMNet城域网与IP专网不直接进行三层连接。

腾讯科技（深圳）有限公司2006年6月RTX2006 管理员手册目录第一章RTX服务器的安装1.1安装所需软硬件环境●RTX服务器网络要求局域网环境；能连接到因特网（可选）；在TCP/IP协议下的固定IP地址，或者动态IP加动态域名；●RTX服务器硬件要求硬盘：5G以上剩余空间；内存：256M以上；CPU：奔腾PentiumⅣ系列，在线人数达到500人以上，上建议使用双CPU服务器；磁盘阵列/光盘库/磁带机（可选）；●RTX服务器软件要求微软视窗系统环境（Windows2000 SERVER以上）；1.2 RTX服务器软件的安装运行“Setup.exe”程序进行安装。

安装过程中涉及“阅读许可协议”、“输入服务与许可证”、“安装路径设置”、“安装后使用者限定”等问题。

如图1.1和图1.2所示：图1.1 阅读软件许可协议图1.2 选择目标目录确定了安装路径之后，点击安装即可安装。

图1.3 设置管理员密码安装到最后一步，将会出现超级管理员帐号设置密码（该将用于安装成功后的用户管理器登录）界面如上图1.3如示；点击下一步往下走，即可完成安装。

第二章RTX服务器端的基本应用本章主要介绍RTX系统的管理端的基本应用，例如管理员登录、设置RTX分组结构、设置RTX 用户账号、内部服务管理等等，通过本章的介绍，管理员已经可以实现RTX在本企业的实际应用。

2.1 管理部门组织结构根据RTX系统的设计原则，RTX客户端用户是不能自动申请RTX号码的。

系统需要由RTX系统管理人员架构企业的部门组织结构、分配RTX号码，然后才可以由RTX客户端进行登录。

本节我们先主要介绍部门组织结构的管理。

2.1.1添加一级部门进入添加一级部门功能有两种主要方法：·从主界面左侧的导航栏定位“RTX用户管理”→“用户管理”→“组织架构”，然后鼠标点击“添加部门”，如下图：图2.1 添加部门的弹出菜单进入添加部门的对话框之后，填写相关资料，然后点击“确定”按钮保存信息，如图所示：图2.2 添加部门信息（注：设定一级部门时，“父部门”一栏一定要选定为“无”。

RTXReal-Time Extension for Controlof Windows技术白皮书美国Ardence公司北京美斯比科技有限公司随着实时嵌入式技术的发展，Windows系列操作系统越来越多的被考虑作为实时系统的应用平台。

为了满足硬实时系统严格的响应时间要求，增加Windows系统的实时能力非常必要。

这篇文章介绍了美国Ardence公司的RTX 产品，RTX在Windows平台上提供了一个实时子系统，实现了确定性的实时线程调度、实时环境和与原始Windows环境之间的进程间通讯机制以及其它只在特定的实时操作系统中才有的对Windows系统的扩展特性。

这篇文章描述了RTX怎样提供这些特性和目前的实时性能，并指出了未来性能增强的方向。

目录简介Windows 平台和实时系统RTX结构深入RTX实时硬件抽象层Windows停止保护扩展HALRTX和中断延迟RTX中断延迟缩减技术RTX对象RTSS调度器服务请求中断Win32到RTSS的IPCRTSS代理模型控制Windows I/O管理器快速计时器支持动态链接库RTSS中的结构异常处理性能使用Visual Studio创建RTX应用程序性能工具目标设计者SLD未来方向结论获取渠道参考微软公司的Windows 系列操作系统的大众接受程度和市场占有率日益扩大。

这主要是基于以下几点原因：∙Windows 平台更强的性能和更低的价格；∙该平台上可运行多种应用程序；∙该平台支持多种开发工具；∙丰富的Win32应用程序接口；∙大量的熟悉本系统的开发支持人员和最终用户。

鉴于多系统的计算环境的复杂度和所需要的额外维护成本，更多的公司倾向于将Windows应用到设备的所有级别上。

将其作为网络服务器或者桌面系统是很容易理解的，因为Windows就是为这些环境而设计的。

但是，仍然有很多其他环境有使用Windows的要求，譬如制造车间，医疗设备，仿真器械，测试设备和通信器材。

NVIDIA RTX 5000 Ada Generation Performance for endless possibilities. DatasheetPowering the Next Era of InnovationIndustries are embracing accelerated computing and AI to tackle powerful dynamics and unlock transformative possibilities. Generative AI is reshapingthe way professionals create and innovate across various domains, from design and engineering to entertainment and healthcare. The NVIDIA RTX™ 5000 Ada Generation GPU, with third-generation RTX technology, unlocks breakthroughsin generative AI, revolutionizing productivity and offering unprecedented creative possibilities.The NVIDIA RTX 5000 Ada Generation GPU is purpose-built for today’s professional workflows. Built on the NVIDIA Ada Lovelace architecture, it combines 100 third-generation RT Cores, 400 fourth-generation Tensor Cores, and 12,800 CUDA® cores with 32 gigabytes (GB) of graphics memory to deliver the next generation of AI graphics and petaFLOPS inferencing performance, accelerating rendering, AI, graphics, and compute workloads. RTX 5000-powered workstations equip you for success in today’s demanding business landscape.NVIDIA RTX professional graphics cards are certified for a broad range of professional applications, tested by leading independent software vendors (ISVs) and workstation manufacturers, and backed by a global team of support specialists. Get the peace of mind to focus on what matters with the premier visual computing solution for mission-critical business.Key Features>PCIe Gen4>Four DisplayPort 1.4a connectors >AV1 encode and decode support >DisplayPort with audio>3D stereo support with stereo connector>NVIDIA® GPUDirect® for Video support>NVIDIA GPUDirect remote direct memory access (RDMA) support >NVIDIA Quadro® Sync II¹ compatibility>NVIDIA RTX Experience>NVIDIA RTX Desktop Manager software>NVIDIA RTX IO support>HDCP 2.2 support>NVIDIA Mosaic² technologyRendering**********************************************(5.2GHzTurbo), 64GB RAM, Windows 11 Enterprise x64, Chaos V-Ray v5.0,NVIDIA Driver 536.15. Relative speedup for 1920x1080 resolution,scene 12 pipeline subtest render time (seconds). Performance basedon pre-released build, subject to change.Omniverse**********************************************(5.2GHzTurbo), 64GB RAM, Windows 11 Enterprise x64, NVIDIA Driver528.49. CAD application performance based on internal testing ofNVIDIA Omniverse Create with several models of varying size andrender complexity. Performance is measured as frames renderedper second. NVIDIA DLSS 3 is enabled for NVIDIA RTX 5000 AdaGeneration GPUs, DLSS 2 enabled for non-Ada generation GPUs.Performance based on pre-released build, subject to change. Training**********************************************(5.2GHzTurbo), 64GB RAM, Windows 11 Enterprise x64, PyTorch v2.1.0,NVIDIA Driver 528.86. Relative speedup for JASPER TrainingPhase, precision = Mixed, batch size = 64. Performance based onpre-released build, subject to change.NVIDIA RTX 5000 Ada Generation | Datasheet | 1Ready to Get Started?To learn more about NVIDIA RTX 5000, visit:/rtx-50001 Quadro Sync II card sold separately. I2 Windows 10 and Linux. I3 Peak rates based on GPU boost clock. I4 Effective FP8teraFLOPS (TFLOPS) using sparsity. I 5 Display ports are on by default for RTX 5000. Display ports aren’t active when usingvGPU software. | 6 Virtualization support for the RTX 5000 Ada Generation GPU will be available in an upcoming NVIDIA vGPUrelease, anticipated in Q3, 2023. | 7 Product is based on a published Khronos specification and is expected to pass the Khronosconformance testing process when available. Current conformance status can be found at /conformance© 2023 NVIDIA Corporation. All rights reserved. NVIDIA, the NVIDIA logo, CUDA, GPUDirect, NVLink, Quadro, and RTX aretrademarks and/or registered trademarks of NVIDIA Corporation in the U.S. and other countries. Other company and productnames may be trademarks of the respective companies with which they are associated. All other trademarks are the propertyof their respective owners. 2788511. JUL23PNY Part Numbers VCNRTX5000ADA-PBYVCNRTX5000ADA-PBVCNRTX5000ADA-EDUVCNRTX5000ADA-BLKVCNRTX5000ADASYNC-PBGPU Memory32GB GDDR6Memory Interface256 bitMemory Bandwidth576GB/sError Correcting Code (ECC)YesNVIDIA Ada LovelaceArchitecture-Based CUDA Cores12,800NVIDIA Fourth-GenerationTensor Cores400NVIDIA Third-Generation RT Cores100Single-Precision Performance65.3 TFLOPS³RT Core Performance151.0 TFLOPS³Tensor Performance1044.4 TFLOPS4System Interface PCIe 4.0 x16Power Consumption Total board power: 250WThermal Solution ActiveForm Factor 4.4” H x 10.5” L, single slotDisplay Connectors4x DisplayPort 1.4a5Max Simultaneous Displays4x 4096 x 2160 @ 120Hz4x 5120 x 2880 @ 60Hz2x 7680 x 4320 @ 60HzEncode/Decode Engines2x encode, 2x decode (+AV1 encodeand decode)VR Ready YesvGPU Software Support6>NVIDIA vPC/vApps>NVIDIA RTX Virtual WorkstationvGPU Profiles Supported See the Virtual GPU licensing guide.Graphics APIs DirectX 12, Shader Model 6.7,OpenGL 4.67, Vulkan 1.37Compute APIs CUDA 12.2, OpenCL 3.0,DirectComputeNVIDIA NVLink ®NoGraphics**********************************************(5.2GHzTurbo), 64GB RAM, Windows 11 Enterprise x64, SPECviewperf2020, NVIDIA Driver 528.49. Relative speedup for 4K SiemensNX composite score. Performance based on pre-released build,subject to change.HPC**********************************************(5.2GHzTurbo), 64GB RAM, Windows 11 Enterprise x64, CUDA 11.8(cuBLAS performance), NVIDIA Driver 525.85. Relative speedupfor GFLOPS, precision = INT8, input = zero. Performance based onpre-released build, subject to change.Generative AI**********************************************(5.2GHzTurbo), 64GB RAM, Windows 11 Enterprise x64, Stable DiffusionWebUI v1.3.1, NVIDIA Driver 536.15. Relative speedup for512x512 image generation. Performance based on pre-releasedbuild, subject to change.。

RTX常见问题解答腾讯科技（深圳）有限公司2014年1月目录一、文档说明 (3)二、RTX服务器防火墙配置 (3)a)RTX服务与端口 (3)b)服务器防火墙设置（以Windows XP SP2为例） (4)三、RTX公网部署 (8)a)RTXServer安装在托管的服务器上 (8)b)RTXServer安装在局域网内的客户机上 (9)c)RTXServer安装在代理服务器上 (10)d)设置公网IP (11)四、远程登录 (12)a)远程登录的原理 (12)b)远程登录优点 (13)c)远程登录功能的限制 (14)d)如何启动OutSupport服务 (14)e)客户端如何使用远程登录 (14)五、SQL数据库配置 (16)a)备份用户数据 (16)b)创建SQL数据库及表 ................................................................ 错误！未定义书签。

c)设置连接字符串 ........................................................................ 错误！未定义书签。

d)导入数据 (17)六、组织架构问题处理 (19)a)组织架构机制 (19)b)组织架构问题表现 (19)c)组织架构问题处理 (19)七、发送文件问题处理 (24)a)发送文件机制 (24)b)发送文件问题表现 (24)c)发送文件问题处理 (24)八、后语 (245)一、文档说明RTX是腾讯公司推出的企业级即时通信平台。

该平台定位于降低企业通信费用，增强企业内部沟通能力，改善企业与客户之间的沟通渠道，创造新兴的企业沟通文化，提高企业生产力。

RTX平台的主要功能，包括企业内部实时信息交互、视频语音、企业短信中心等等。

RTX平台具有很高的实用性、易用性、可管理性和安全性。

除了底层采用128位对称加密技术之外，在实际应用中，RTX可以通过员工实名制、记录对外交互信息等措施，确保企业应用的通信安全。

DU-09454-001 _v10 | October 2022 Quick Start GuideTABLE OF CONTENTS Chapter 1. Creating and Using an NVIDIA RTX Virtual Workstation Instance from the Google Cloud Platform Marketplace (1)1.1. Creating a GPU-Accelerated Virtual Workstation (1)1.2. Connecting to a GPU-Accelerated Virtual Workstation on a Windows VM Instance (3)1.3. Verifying the Creation of your GPU-Accelerated Virtual Workstation (4)1.3.1. Verifying the Creation of a GPU-Accelerated Virtual Workstation on a Windows VMInstance (4)1.3.2. Verifying the Creation of a GPU-Accelerated Virtual Workstation on a Linux VMInstance (5)1.4. Trying Out your GPU-Accelerated Virtual Workstation (6)1.4.1. Trying Out your GPU-Accelerated Virtual Workstation with Adobe Premiere Pro (6)1.4.2. Trying Out your GPU-Accelerated Virtual Workstation with Esri ArcGIS Pro (6)Appendix A. Resources for NVIDIA RTX Virtual Workstation on Google Cloud Platform (7)NVIDIA® RTX™ Virtual Workstation in the cloud is an NVIDIA Virtual Machine Image (VMI) preconfigured with NVIDIA RTX Virtual Workstation software and NVIDIA GPU hardware. The NVIDIA RTX Enterprise driver is preinstalled on the VMI and NVIDIA ensures that the image is always up to date with the latest NVIDIA RTXISV certifications, patches, and upgrades. Support and technical information to help you get started are available on the NVIDIA RTX Virtual Workstation (vWS) on CSP Marketplace community forum and from additional resources.1.1. Creating a GPU-Accelerated Virtual WorkstationPrepare for creating a GPU-accelerated virtual workstation from the Google Cloud Platform marketplace as follows:‣Ensure that you have a Google Cloud Platform account and an active subscription.‣Decide the machine type of the VM instance on which you want to deploy your GPU-accelerated virtual workstation.For information about the machine types that support NVIDIA RTX VirtualWorkstation, see NVIDIA RTX Virtual Workstation on Google Cloud Platform Release Notes.‣If you are creating an Ubuntu VM instance and want to use SSH public key authentication for the administrator account, generate an SSH public key.1.In a web browser, log on to Google Cloud Platform portal (https:///).2.Go to the NVIDIA RTX Virtual Workstation listing for the guest OS that you want touse:‣NVIDIA RTX Virtual Workstation - Windows Server 2019‣NVIDIA RTX Virtual Workstation - Windows Server 2016‣NVIDIA RTX Virtual Workstation - Ubuntu 18.043.Click LAUNCH ON COMPUTE ENGINE to go to the New deployment page forNVIDIA RTX Virtual Workstation on your chosen operating system.4.On the New deployment page, provide the project details and instance details.a)From the drop-down list at the top of the page, select your project.b)If you don't want the default assigned deployment name, enter the name that youwant for your NVIDIA RTX Virtual Workstation instance.c)Select the zone for your NVIDIA RTX Virtual Workstation instance.Ensure that the GPU type that you want is available in the selected zone. Not all GPU types that support NVIDIA RTX Virtual Workstation are available in allzones.d)From the Number of GPUs drop-down list, select the number of GPUs that youwant for your instance.The number of number of GPUs selected limits the number of vCPUs allowed in your machine type. A maximum of 16 vCPUs is allowed for each GPU.e)From the GPU Type drop-down list, select the GPU type that you want for yourinstance.If the GPU type that you want to use is not listed, select a different zone until you find the GPU type that you want.f)Specify the machine type with which you want to create your instance.‣To create an instance with a predefined machine type, from the drop-down list under Machine type, select the machine type that you want.‣To create an instance with custom virtualized hardware settings, follow the Customize link.For guidance, see the following Google Cloud Platform documentation:‣GPUs on Compute Engine‣Regions and Zones‣Machine Types‣Creating a VM Instance with a Custom Machine Type5.In the Boot Disk and Networking sections, review the default settings to see if theymeet your requirements and change any settings as required.For guidance, see the following Google Cloud Platform documentation:‣Storage Options‣Virtual Private Cloud (VPC) Network Overview6.Click Deploy.Deployment is complete in a few minutes.‣If you created a Windows VM instance, follow these instructions to create a password for the instance and connect to the VM instance:1.Creating Passwords for Windows Instances in the Google Cloud Platformdocumentation2.Connecting to a GPU-Accelerated Virtual Workstation on a Windows VMInstance‣If you created a Linux VM instance, follow these instructions to connect to the VM instance and verify that it was created properly:1.Connecting to Linux instances in the Google Cloud Platform documentation2.Verifying the Creation of your GPU-Accelerated Virtual Workstation1.2. Connecting to a GPU-Accelerated Virtual Workstation on a Windows VM InstanceFor instructions for an Ubuntu VM instance, see in Connecting to Linux instances inthe Google Cloud Platform documentation.Ensure that you have created a windows instance password as explain in Creating Passwords for Windows Instances in the Google Cloud Platform documentation.1.On the Google Cloud Platform console VM instances page page, from the RDPdrop-down list, select Download the RDP file.2.When the download is complete, double-click the RDP file to start a RemoteDesktop Connection session on the VM.3.If you are warned that the publisher of the remote connection cannot be identifiedand are asked about whether to connect anyway, click Connect.4.When you are prompted, log in to the VM with the credentials for the account thatyou specified when you created the password for your Windows VM Instance.5.If you are warned that the publisher of the remote connection cannot be identifiedand are asked about whether to connect anyway, click Yes.After connecting to your GPU-accelerated virtual workstation, verify that it was created properly as explained in Verifying the Creation of your GPU-Accelerated Virtual Workstation.1.3. Verifying the Creation of your GPU-Accelerated Virtual WorkstationAfter connecting to your GPU-accelerated virtual workstation, verify that it was created properly by listing its GPUs. On a Windows VM instance you can also use NVIDIA Control Panel to verify that the NVIDIA driver is running.For details, see:‣Verifying the Creation of a GPU-Accelerated Virtual Workstation on a Windows VM Instance‣Verifying the Creation of a GPU-Accelerated Virtual Workstation on a Linux VM InstanceAfter verifying the creation of your GPU-accelerated virtual workstation, you are now ready to run your design and engineering software.1.3.1. Verifying the Creation of a GPU-Accelerated Virtual Workstation on a Windows VM Instance1.Open a command prompt window and change to the folder that contains thenvidia-smi command.C:\Program Files (x86)\Google\Cloud SDK>cd C:\Program Files\NVIDIACorporation\NVSMI2.List the GPUs in your GPU-accelerated virtual workstation by running the nvidia-smi command without any options.The following example shows the output from nvidia-smi for a Windows VM instance configured with a single NVIDIA T4 GPU.C:\Program Files\NVIDIA Corporation\NVSMI>nvidia-smiWed Apr 03 01:49:00 2019+--------------------------------------------------------------------------+ | NVIDIA-SMI 412.16 Driver Version: 412.16 CUDA Version: 10.0 | |-----------------------------+---------------------+----------------------+ | GPU Name TCC/WDDM | Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | |=============================+=====================+======================| | 0 Tesla T4 WDDM | 0000000:00:04.0 Off | 0 | | N/A 36C P8 12W / 70W | 310MiB / 15360MiB | 16% Default | +-----------------------------+---------------------+----------------------+ +--------------------------------------------------------------------------+ | Processes: GPU Memory | | GPU PID Type Process name Usage | |==========================================================================| | 0 404 C+G Insufficient Permissions N/A | | 0 896 C+G Insufficient Permissions N/A | | 0 908 C+G Insufficient Permissions N/A | | 0 2340 C+G C:\Windows\explorer.exe N/A | | 0 4304 C+G ...ration\Control Panel Client\nvcplui.exe N/A | | 0 4624 C+G ...dows.Cortana_cw5n1h2txyewy\SearchUI.exe N/A || 0 5116 C+G ...t_cw5n1h2txyewy\ShellExperienceHost.exe N/A | +--------------------------------------------------------------------------+3.Start NVIDIA Control Panel to verify that the NVIDIA driver is running.a)Right-click on the desktop.b)From the menu that opens, choose NVIDIA Control Panel.4.In the NVIDIA Control Panel, from the Help menu, choose System Information toget information about the GPU.NVIDIA Control Panel reports the GPU that is being used, its capabilities, and the NVIDIA driver version that is loaded.1.3.2. Verifying the Creation of a GPU-Accelerated Virtual Workstation on a Linux VM InstanceList the GPUs in your GPU-accelerated virtual workstation by running the nvidia-smi command without any options.The following example shows the output from nvidia-smi for a Linux VM instance configured with a single NVIDIA P100 GPU.qvwsuser@nvidia-quadro-virtual-workstation-with-ubuntu-1-vm:~$ nvidia-smi Wed Apr 3 02:01:30 2019+-----------------------------------------------------------------------------+ | NVIDIA-SMI 410.92 Driver Version: 410.92 CUDA Version: 10.0 | |-------------------------------+----------------------+----------------------+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | |===============================+======================+======================| | 0 Tesla P100-PCIE... On | 00000000:00:04.0 Off | 0 | | N/A 33C P0 26W / 250W | 0MiB / 16280MiB | 0% Default | +-------------------------------+----------------------+----------------------+ +-----------------------------------------------------------------------------+ | Processes: GPU Memory | | GPU PID Type Process name Usage | |=============================================================================| | No running processes found | +-----------------------------------------------------------------------------+qvwsuser@nvidia-quadro-virtual-workstation-with-ubuntu-1-vm:~$1.4. Trying Out your GPU-Accelerated Virtual WorkstationSome third-party applicaton vendors have endorsed the use of some of their appswith NVIDIA RTX Virtual Workstation on Google Cloud Platform. If you can installan endorsed app in your VM, you can use the app to try out your cloud-based virtual workstation.1.4.1. Trying Out your GPU-Accelerated Virtual Workstation with Adobe Premiere ProAdobe has endorsed the use of the Adobe Premiere Pro video editing app with NVIDIA RTX Virtual Workstation on Google Cloud Platform. If you can install the Adobe Premiere Pro app in your VM, you can use the app to try out your cloud-based virtual workstation.1.Open the sample project in Adobe Premiere Pro.2.Scrub through the video and see that the video continues to play smoothly andwithout stutter.3.Adjust colors, contrast, and shadows and notice that the adjustments are appliedinstantaneously.4.Play the video and see that playback is remarkably smooth with no dropped frames.1.4.2. Trying Out your GPU-Accelerated Virtual Workstation with Esri ArcGIS ProEsri has endorsed the use of the Esri ArcGIS Pro app with NVIDIA RTX Virtual Workstation on Google Cloud Platform. If you can install the Esri ArcGIS Pro app in your VM, you can use the app to try out your cloud-based virtual workstation.1.Open Esri ArcGIS Pro.2.View geographic data and notice the responsiveness of the app.e Detect Objects Using Deep Learning to detect palm trees.The geoprocessing feature provided by Esri deep learning tools uses inferencing to detect palm trees.With NVIDIA RTX Virtual Workstation images from the Google Cloud Platform marketplace powered by NVIDIA GPUs, you can run complex graphics visualization applications with AI-enhanced features from anywhere.‣NVIDIA RTX Virtual Workstation on Google Cloud Platform Release Notes‣NVIDIA RTX Virtual Workstation listings on Google Cloud Platform marketplace:‣NVIDIA RTX Virtual Workstation - Windows Server 2019‣NVIDIA RTX Virtual Workstation - Windows Server 2016‣NVIDIA RTX Virtual Workstation - Ubuntu 18.04‣Google Cloud Platform documentation:‣GPUs on Compute Engine‣Regions and Zones‣Machine Types‣Creating a VM Instance with a Custom Machine Type‣Storage Options‣Virtual Private Cloud (VPC) Network Overview‣Creating Passwords for Windows Instances‣Connecting to Linux instancesNoticeThis document is provided for information purposes only and shall not be regarded as a warranty of a certain functionality, condition, or quality of a product. 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利妥昔单抗与自身免疫性疾病张慧儒;吕会新【摘要】利妥昔单抗（ rituximab，RTX），又名美罗华，它是一种人鼠嵌合型单克隆抗体，它通过阻断CD20单克隆抗体而起作用，最早被用于淋巴瘤的治疗。

随着其作用机制进一步认识，逐渐被用于一些自身免疫性疾病以及一些难治、复发的肾病综合症的治疗。

为了加深对利妥昔单抗的认识，本文就其在自身免疫性疾病方面的应用做一综述。

【期刊名称】《河北医药》【年(卷),期】2016(038)016【总页数】4页(P2522-2524,2525)【关键词】利妥昔单抗;肾病;狼疮性肾炎;免疫性血小板减少性紫癜【作者】张慧儒;吕会新【作者单位】050011 河北省石家庄市第一医院血液内科，营养科;050011 河北省石家庄市第一医院血液内科，营养科【正文语种】中文【中图分类】R593利妥昔单抗(rituximab，RTX)，又名美罗华，是一种人鼠嵌合型单克隆抗体，通过基因重组技术研究成功，是最先被用于治疗肿瘤的单抗。

它由人IgG1抗体Fc段和鼠CD20单克隆抗体Fab段构成。

其宿主效应机能由人源性区域介导产生。

它由氨基酸1 328个组成，144 000的相对分子量，由可变区Fab即抗鼠CD20单克隆抗体和恒定区Fc片段即人IgG1抗体组成。

美罗华可以和成熟B淋巴细胞和前B细胞膜表面的CD20抗原特异性结合，引起免疫反应，而该反应在成熟后B淋巴细胞、造血干细胞等其他正常组织中不存在。

自1997年美罗华获FDA批准上市后，已在临床及科研中显示了很好疗效，大大提高了淋巴瘤的缓解率和生存时间。

利妥昔单抗是最早成功应用于治疗恶性肿瘤的单克隆抗体之一。

近年来，它的应用范围被进一步推广，包括难治性和复发性肾病综合征等。

由于免疫性血小板减少性紫癜(thrombotic thrombocytopenic purpura,ITP)常伴有功能异常的B 细胞，产生异常的血小板抗体，出现了ITP。

此异常的B淋巴细胞可以被利妥昔单抗清除，所以利妥昔单抗可用于治疗难治、复发的ITP的治疗。

櫄櫄櫄櫄櫄櫄櫄櫄櫄櫄櫄櫄櫄櫄櫄櫄櫄櫄櫄櫄櫄櫄櫄櫄櫄櫄櫄櫄櫄櫄櫄櫄櫄櫄櫄櫄櫄櫄櫄櫄櫄櫄櫄櫄櫄櫄［２１］杨　坚，钱　振，张燕军，等．采用改进ＹＯＬＯｖ４－ｔｉｎｙ的复杂环境下番茄实时识别［Ｊ］．农业工程学报，２０２２，３８（９）：２１５－２２１．　［２２］ＹａｎｇＬ，ＺｈａｎｇＲＹ，ＬｉＬ，ｅｔａｌ．Ｓｉｍａｍ：ａｓｉｍｐｌｅ，ｐａｒａｍｅｔｅｒ－ｆｒｅｅａｔｔｅｎｔｉｏｎｍｏｄｕｌｅｆｏｒｃｏｎｖｏｌｕｔｉｏｎａｌｎｅｕｒａｌｎｅｔｗｏｒｋｓ［Ｃ］／／ＰｒｏｃｅｅｄｉｎｇｓｏｆｔｈｅＩＥＥＥＩｎｔｅｒｎａｔｉｏｎａｌＣｏｎｆｅｒｅｎｃｅｏｎＭａｃｈｉｎｅＬｅａｒｎｉｎｇ．Ｖｉｅｎｎａ，２０２１：１１８６３－１１８７４．［２３］袁自然，叶　寅，武　际，等．基于高光谱成像技术的辣椒叶片叶绿素含量估算［Ｊ］．江苏农业科学，２０２１，４９（１６）：１８９－１９３．［２４］ＨｅＫＭ，ＺｈａｎｇＸＹ，ＲｅｎＳＱ，ｅｔａｌ．Ｄｅｌｖｉｎｇｄｅｅｐｉｎｔｏｒｅｃｔｉｆｉｅｒｓ：ｓｕｒｐａｓｓｉｎｇｈｕｍａｎ－ｌｅｖｅｌｐｅｒｆｏｒｍａｎｃｅｏｎＩｍａｇｅＮｅｔｃｌａｓｓｉｆｉｃａｔｉｏｎ［Ｃ］／／２０１５ＩＥＥＥＩｎｔｅｒｎａｔｉｏｎａｌＣｏｎｆｅｒｅｎｃｅｏｎＣｏｍｐｕｔｅｒＶｉｓｉｏｎ．Ｓａｎｔｉａｇｏ，２０１５：１０２６－１０３４．［２５］金松林，来纯晓，郑　颖，等．基于特征选择和ＣＮＮ＋Ｂｉ－ＲＮＮ模型的小麦抗寒性识别方法［Ｊ］．江苏农业科学，２０２２，５０（１０）：２０１－２０７．［２６］陕西省统计局，国家统计局陕西调查总队．陕西统计年鉴：２０２１［Ｍ］．北京：中国统计出版社，２０２１．周绍发，肖小玲，刘忠意，等．改进的基于ＹＯＬＯｖ５ｓ苹果树叶病害检测［Ｊ］．江苏农业科学，２０２３，５１（１３）：２１２－２２０．ｄｏｉ：１０．１５８８９／ｊ．ｉｓｓｎ．１００２－１３０２．２０２３．１３．０３２改进的基于ＹＯＬＯｖ５ｓ苹果树叶病害检测周绍发，肖小玲，刘忠意，鲁　力（长江大学计算机科学学院，湖北荆州４３４０００） 摘要：针对目前在复杂环境下苹果树叶病害检测准确度低、鲁棒性差、计算量大等问题，提出一种改进的基于ＹＯＬＯｖ５ｓ苹果树叶病害的检测方法。