Not all networks are created equal. Brocade Ethernet Fabrics, as joined in the IBM Flex EN4023 embedded switch, revolutionizes by automating and optimizing your network, enabling you to reduce total cost of ownership, not just capital expenses. Lab tests have validated the Opex and Capex advantages of VCS Ethernet Fabrics over traditional networking. Learn how customers have reduced network infrastructure requirements by 25% and increases the networks performance by up to 30%. See how Dynamic Ports on Demand can save hardware costs. Learn the dramatic Operational impact VCS Fabric switches have on decreasing time to deploy the network by 79% and decreasing the time to implement network changes by 85%.
SAN Health is a free Brocade utility that provides a comprehensive point-in-time report on your SAN. SAN Health provides a data capture application that is executed against the SAN, and a back-end report processing engine, that provides an extensive detail on the SAN configuration. Topics covered, learn how to generate the SAN Heath reports to ensure the SAN infrastructure is optimized to support IBM FlashSystems, all IBM Storage products, and the new FICON SAN Health Utility
Two Fast Paths to Docker Networking with Brocade VDXBrocade
Wondering how to get started with Docker Networking? We share 2 approaches to get Docker software up and running alongside a Brocade VDX switch fabric.
Find a detailed blog, here: http://bit.ly/2dxOWIO
Five Networking Must - Haves For ContainersBrocade
Businesses have unique requirements as they move to use container technologies, we believe these five must-haves will be critical to drive maximum benefits from container adoption.
Read the blog on this topic here: http://bit.ly/2bzmNBw
Let the conversation flow with Brocade vADCBrocade
Brocade vADC solutions deliver high application availability on demand. Streamline collaboration and open communication channels to improve business efficiency.
Download for links to: UBM paper, Colorcon Success Story, Trial Software
Always-on performance for Always-on BusinessBrocade
Make sure your critical business applications meet performance demands with Brocade vADC solutions.
Download for links to ESG Research paper, SUNY Success Story, and Trial Software.
Recent Advances in Machine Learning: Bringing a New Level of Intelligence to ...Brocade
Presentation by Brocade Chief Scientist and Fellow, David Meyer, given at Orange Gardens July 2016. What is Machine Learning and what is all the excitement about?
An associated blog is available here: http://community.brocade.com/t5/CTO-Corner/Networking-Meets-Artificial-Intelligence-A-Glimpse-into-the-Very/ba-p/88196
VM Farms Thrive with Dedicated IP Storage NetworksBrocade
Is your VM farm in hyper growth? Is it slowing you down? See how to boost performance for VM farms with a dedicated IP storage network from Brocade and EMC. Benefit from a network that keeps up with apps and delivers on customer SLAs.
セル生産方式におけるロボットの活用には様々な問題があるが,その一つとして 3 体以上の物体の組み立てが挙げられる.一般に,複数物体を同時に組み立てる際は,対象の部品をそれぞれロボットアームまたは治具でそれぞれ独立に保持することで組み立てを遂行すると考えられる.ただし,この方法ではロボットアームや治具を部品数と同じ数だけ必要とし,部品数が多いほどコスト面や設置スペースの関係で無駄が多くなる.この課題に対して音𣷓らは組み立て対象物に働く接触力等の解析により,治具等で固定されていない対象物が組み立て作業中に運動しにくい状態となる条件を求めた.すなわち,環境中の非把持対象物のロバスト性を考慮して,組み立て作業条件を検討している.本研究ではこの方策に基づいて,複数物体の組み立て作業を単腕マニピュレータで実行することを目的とする.このとき,対象物のロバスト性を考慮することで,仮組状態の複数物体を同時に扱う手法を提案する.作業対象としてパイプジョイントの組み立てを挙げ,簡易な道具を用いることで単腕マニピュレータで複数物体を同時に把持できることを示す.さらに,作業成功率の向上のために RGB-D カメラを用いた物体の位置検出に基づくロボット制御及び動作計画を実装する.
This paper discusses assembly operations using a single manipulator and a parallel gripper to simultaneously
grasp multiple objects and hold the group of temporarily assembled objects. Multiple robots and jigs generally operate
assembly tasks by constraining the target objects mechanically or geometrically to prevent them from moving. It is
necessary to analyze the physical interaction between the objects for such constraints to achieve the tasks with a single
gripper. In this paper, we focus on assembling pipe joints as an example and discuss constraining the motion of the
objects. Our demonstration shows that a simple tool can facilitate holding multiple objects with a single gripper.
【DLゼミ】XFeat: Accelerated Features for Lightweight Image Matchingharmonylab
公開URL:https://arxiv.org/pdf/2404.19174
出典:Guilherme Potje, Felipe Cadar, Andre Araujo, Renato Martins, Erickson R. ascimento: XFeat: Accelerated Features for Lightweight Image Matching, Proceedings of the 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023)
概要:リソース効率に優れた特徴点マッチングのための軽量なアーキテクチャ「XFeat(Accelerated Features)」を提案します。手法は、局所的な特徴点の検出、抽出、マッチングのための畳み込みニューラルネットワークの基本的な設計を再検討します。特に、リソースが限られたデバイス向けに迅速かつ堅牢なアルゴリズムが必要とされるため、解像度を可能な限り高く保ちながら、ネットワークのチャネル数を制限します。さらに、スパース下でのマッチングを選択できる設計となっており、ナビゲーションやARなどのアプリケーションに適しています。XFeatは、高速かつ同等以上の精度を実現し、一般的なラップトップのCPU上でリアルタイムで動作します。