Thank you communication network in organization 感謝ネットワークからみる組織のコミュニケーションの形Hiroko Onari
感謝ネットワークからみる組織のコミュニケーションの形
Thank you communication network in organization.
Engaged employees tend to say "thank you" with the reason of the appreciation. The managers who have an excellent vocabulary motivate and inspire their subordinates.
Altmetrics presentation mla'14 japanese version: オルトメトリックスとその他の研究影響度の指 標 はどう違...Lilian Takahashi Hoffecker
This set of powerpoint slides summarizes our pilot study examining two altmetric gathering products PlumX (Plum Analytics) with additional information on Altmetric.com (MacMillan). We had Plum Analytics create profiles for several University of Colorado faculty. The faculty provided us with feedback on their social media visibility, or lack of it. The original English presentation is translated into three languages: Russian, Chinese and Japanese.
Thank you communication network in organization 感謝ネットワークからみる組織のコミュニケーションの形Hiroko Onari
感謝ネットワークからみる組織のコミュニケーションの形
Thank you communication network in organization.
Engaged employees tend to say "thank you" with the reason of the appreciation. The managers who have an excellent vocabulary motivate and inspire their subordinates.
Altmetrics presentation mla'14 japanese version: オルトメトリックスとその他の研究影響度の指 標 はどう違...Lilian Takahashi Hoffecker
This set of powerpoint slides summarizes our pilot study examining two altmetric gathering products PlumX (Plum Analytics) with additional information on Altmetric.com (MacMillan). We had Plum Analytics create profiles for several University of Colorado faculty. The faculty provided us with feedback on their social media visibility, or lack of it. The original English presentation is translated into three languages: Russian, Chinese and Japanese.
Similar to テキストアナリティクスの知見を社会に活かすには? -シーズ指向の視点とニーズ指向の視点- (20)
Selection of housing, one of the necessities of human life, has a great influence on life for a long time. However, since it requires a wide range of information gathering and consideration before decision, state-of-the-art recommendation algorithms such as collaborative filtering do not work well. In this presentation, after reviewing issues specific to the real estate field, I cited examples of "application of crowdsourcing to social media (Twitter timelines)" and "application of deep learning to property images" as an effort by our research group. Finally I discuss what kind of AI technology is applicable in the real estate field.
Mining User Experience through Crowdsourcing: A Property Search Behavior Corp...Yoji Kiyota
This article describes how to build a property search behavior corpus derived from microblogging timelines, in which tweets related to property search are annotated. We applied microtask-based crowdsourcing to tweet data, and build a corpus which consists of timelines of specific users which are annotated with property search stages (e.g. gathering of property information, and property preview). As a result, property search processes by tens of people were annotated. This corpus is intended to use for redesigning property information services, and marketing information services for potential users.
【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。
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.