LODチャレンジ実行委員会 関西支部長 古崎晃司
LODチャレンジ実行委員会/Linked Open Data Initiative理事 松村冬子
Linked Open Dataの基本的な技術の解説,利用事例の紹介に加え,簡単なサンプルプログラムの紹介など,ハッカソンに活用できるLOD技術や情報ソースについて解説します.
第3回Linked Open Dataハッカソン関西(1日目)アイデアソン
開催日:2014年2月11日(火)
情報システム障害解析のための知識グラフ構築の試み / Constructing a knowledge graph for information sys...Shinji Takao
人工知能学会 第25回知識流通ネットワーク研究会発表 http://sigksn.html.xdomain.jp/conf25/index.html
システム障害解析に関する専門家知識の抽出、グラフ化、DB化を行った際得られた知見と、知識流通手段としての知識グラフの可能性と課題を考察した結果を報告します。
Knowledge graphs have been getting attention because of its relevance to interpretable AI. Not only that, they also can be useful as a knowledge sharing mean which enable non-experts to utilize experts’ knowledge. We aim to report findings from constructing a knowledge graph through eliciting experts’ knowledge and building a knowledge database. We also suggest the possibilities and issues of knowledge graph as a knowledge sharing mean.
LODチャレンジ実行委員会 関西支部長 古崎晃司
LODチャレンジ実行委員会/Linked Open Data Initiative理事 松村冬子
Linked Open Dataの基本的な技術の解説,利用事例の紹介に加え,簡単なサンプルプログラムの紹介など,ハッカソンに活用できるLOD技術や情報ソースについて解説します.
第3回Linked Open Dataハッカソン関西(1日目)アイデアソン
開催日:2014年2月11日(火)
情報システム障害解析のための知識グラフ構築の試み / Constructing a knowledge graph for information sys...Shinji Takao
人工知能学会 第25回知識流通ネットワーク研究会発表 http://sigksn.html.xdomain.jp/conf25/index.html
システム障害解析に関する専門家知識の抽出、グラフ化、DB化を行った際得られた知見と、知識流通手段としての知識グラフの可能性と課題を考察した結果を報告します。
Knowledge graphs have been getting attention because of its relevance to interpretable AI. Not only that, they also can be useful as a knowledge sharing mean which enable non-experts to utilize experts’ knowledge. We aim to report findings from constructing a knowledge graph through eliciting experts’ knowledge and building a knowledge database. We also suggest the possibilities and issues of knowledge graph as a knowledge sharing mean.
Presented at Journal Paper Track, The Web Conference, Lyon, France, April 15, 2018
https://doi.org/10.1145/3184558.3186234
Abstract: Linked Open Data (LOD) technology enables web of data and exchangeable knowledge graphs through the Internet. However, the change in knowledge is happened everywhere and every time, and it becomes a challenging issue of linking data precisely because the misinterpretation and misunderstanding of some terms and concepts may be dissimilar under different context of time and different community knowledge. To solve this issue, we introduce an approach to the preservation of knowledge graph, and we select the biodiversity domain to be our case studies because knowledge of this domain is commonly changed and all changes are clearly documented. Our work produces an ontology, transformation rules, and an application to demonstrate that it is feasible to present and preserve knowledge graphs and provides open and accurate access to linked data. It covers changes in names and their relationships from different time and communities as can be seen in the cases of taxonomic knowledge.
We propose Crop Vocabulary(CVO) as a basis of the core vocabulary of crop names that becomes the guidelines for data interoperability between agricultural ICT systems on the food chain. Since a single species is treated in different ways, there are many different types of crop names. So, we organize the crop name discriminated by properties such as scientific name, planting method, edible part and registered cultivar information. Also, Crop Vocabulary is also linked to existing vocabularies issued by Japanese government agency and international organization such as AGROVOC. It is expected to use in the data format in the agricultural ICT system.
Presented in 45th Asia Pacific Advanced Network (APAN45) Meeting, Singapore (2018)
Presented as the invited talk at International Workshop on kNowledge eXplication for Industry (kNeXI2017). In this talk, I explain the experience and lesson learnt how to build ontologies. I am currently building the agriculture activity ontology (AAO). It describes classification and properties of various activities in the agriculture domain. It is formalized with Description Logics.
Presented at the Interest Group on Agricultural Data (IGAD) ,3 April, 2017, Barcelona, Spain
Abstract: n this talk, we present the current status of our agriculture ontologies that are developed to accelerate the data use in agriculture.
The agriculture activity ontology formalizes the activities in agriculture. We have developed it for three years. Now we are developing its applications. One application is to exchange formats between different farmer management systems. Another ontology is the crop ontology that standardizes the names of crops. The structure is simple but has links to many other standards in distribution industry, food industry and so on.
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。