SBIR Phase II: Supply Chain Optimization and Product Explorer

SBIR 第二阶段：供应链优化和产品探索

• 批准号: 0620233
• 负责人: Nainesh Rathod
• 金额: $ 50万
• 依托单位: IMAGINESTICS LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-08-01 至 2012-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0620233&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Supply; Chain

This Small Business Innovation Research Phase II project will achieve higher retrieval accuracy for shape-based search for both the web and the enterprise. The proposed work in Phase II is to achieve higher retrieval accuracy supported by three key components: 1) pose determination for 3D models: bridging the space gap between 2D and 3D shapes by finding three intuitive and robust orthogonal orientations for 3D models; 2) 2D orthogonal view generation: representing a three orthogonal views along the pose orientations; 3) similarity measurement between 2D shapes: finding 2D and 3D shapes based on the user's query. A framework will be developed by focusing on three important modules: 1) 2D constraint detection and use of implied constraints with initial application in 2D and 3D views; (2) Enhanced multiple level-of-details in 3D representations, and (3) Human assisted system classification of large datasets. Traditional options of finding part suppliers using catalogs, trade shows and prior business relationship limit the choice of suppliers. Current text-based search to find suppliers face challenges, such as context and language sensitivity, and is inadequate in overcoming the technological challenges posed by variations in how product or part information is specified across a global supply chain. The current effort proposes to use shape, which is the lowest common denominator, to link the OEMs and suppliers. This technology can also aid the current trend among companies in aerospace, automotive, medical equipments and other industries towards 3Ddata standards for fast retrieval, as it can provide a significant leap in terms of accuracy, speed and relevance in the search and retrieval of information. If successful, this technology can contribute significantly to research in areas where shape is important, such as bio-technology and pharmaceutical sectors, where rapid identification of molecules and their docking features help reduce time and cost involved in drug development. For the medical industry due to increased usage of CT scans and 3D imaging technologies, 3D shape search can be used for local feature identification in colonoscopy or other exploratory procedures, brain angiography, reconstruction, projection of malformation or location of polyps and ensure better and rapid diagnosis of disease. Development of methods for automatically parsing human sketches and determining constraints will enable many other research activities and broadly help in a more natural human machine interaction.

这个小企业创新研究二期项目将为基于形状的网络和企业搜索实现更高的检索精度。第二阶段的工作是通过三个关键组件来实现更高的检索精度：1)三维模型的姿态确定：通过寻找三维模型的三个直观且鲁棒的正交方向来弥合二维和三维形状之间的空间差距；2)二维正交视图生成：表示沿位姿方向的三个正交视图；3) 2D形状之间的相似性度量：根据用户的查询找到2D和3D形状。将通过关注三个重要模块来开发框架：1)2D约束检测和使用隐含约束，初始应用于2D和3D视图；(2)增强了三维表示中的多层次细节；(3)大型数据集的人工辅助系统分类。使用目录、贸易展览和先前的业务关系寻找零件供应商的传统选择限制了供应商的选择。当前基于文本的寻找供应商的搜索面临着挑战，例如上下文和语言敏感性，并且不足以克服全球供应链中产品或部件信息如何指定的变化所带来的技术挑战。目前的努力建议使用形状，这是最小的公分母，连接oem和供应商。该技术还可以帮助航空航天，汽车，医疗设备等行业的公司实现快速检索的3d数据标准，因为它可以在搜索和检索信息的准确性，速度和相关性方面提供重大飞跃。如果成功，这项技术可以为形状重要的领域的研究做出重大贡献，例如生物技术和制药领域，在这些领域，分子的快速识别及其对接特性有助于减少药物开发的时间和成本。对于医疗行业，由于CT扫描和3D成像技术的使用增加，3D形状搜索可用于结肠镜检查或其他探查程序，脑血管造影，重建，畸形投影或息肉定位的局部特征识别，并确保更好，快速地诊断疾病。自动解析人类草图和确定约束条件的方法的发展将使许多其他研究活动成为可能，并广泛地帮助实现更自然的人机交互。