SBIR Phase I: Adaptive Hybrid Intelligent Systems Managing Uncertainty in Operational Environments

SBIR 第一阶段：自适应混合智能系统管理操作环境中的不确定性

• 批准号: 1215063
• 负责人: Emory Fry
• 金额: $ 14.95万
• 依托单位: Cognitive Medical Systems
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-07-01 至 2012-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1215063&HistoricalAwards=false
• 关键词: SBIR; Phase; Adaptive; Hybrid; Intelligent

This Small Business Innovation Research (SBIR) Phase I project proposes to research improvements in rule engine technologies that enable the development of autonomous "smart systems" that can identify when and how they must adapt to unexpected environmental conditions. Reasoning technologies are important in a variety of commercial systems providing valuable control, monitoring, and data analysis capabilities. Within healthcare, they provide indispensable services for patient monitoring, disease surveillance, and decision support, etc., allowing such systems to operate reliably and predictably. Nevertheless, real-world environments are characterized by unexpected events that autonomous systems find difficult to recognize and compensate for without human assistance. These challenges suggest the need for a high-level Management Component capable of identifying when operational conditions change and when the system must re-configure or re-train its control algorithms. We will research the development of adaptive hybrid intelligent systems combining two or more reasoning technologies into a general-purpose management framework that promises to improve an autonomous system's responsiveness and adaptability.The broader impact/commercial potential of this project can be readily illustrated within the healthcare market where the volume of patient data now being generated cannot reliably be analyzed by any one provider. New clinical decision support technologies and products are desperately needed to process and reason over complex patient data and to assist clinicians in making appropriate decisions. If successful, the proposed hybrid intelligent system architecture is potentially applicable to many medical devices. For example, gas blenders that are currently adjusted manually to maintain adequate patient oxygen saturation could be servo controlled with algorithms that safely detect and alarm when simply increasing gas delivery is not appropriate. Furthermore, the utility of hybrid intelligent system architectures is not specific to the medical domain. Any use case where contextual awareness is required for optimal performance of a control algorithm or model could be a potential candidate. Hybrid control architectures promise to reduce the dependency that such systems have on human oversight, and if appropriately applied, could reduce human error, improve cost-effectiveness and yet still maintain quality standards.

这个小企业创新研究（SBIR）第一阶段项目提出研究规则引擎技术的改进，使自主“智能系统”的发展，可以确定何时以及如何适应意外的环境条件。推理技术在提供有价值的控制、监视和数据分析能力的各种商业系统中是重要的。在医疗保健领域，它们为患者监测、疾病监测和决策支持等提供不可或缺的服务，允许这种系统可靠地和可预测地操作。然而，现实世界环境的特点是意外事件，自治系统发现难以识别和补偿没有人类的帮助。这些挑战表明，需要一个高层次的管理组件，能够识别何时操作条件发生变化，以及何时系统必须重新配置或重新训练其控制算法。 我们将研究开发自适应混合智能系统，将两种或两种以上的推理技术结合到一个通用的管理框架中，以提高自主系统的响应能力和适应性。该项目的更广泛的影响/商业潜力可以很容易地在医疗保健市场中得到说明，现在生成的患者数据量无法由任何一个提供商进行可靠的分析。迫切需要新的临床决策支持技术和产品来处理和推理复杂的患者数据，并帮助临床医生做出适当的决策。如果成功，所提出的混合智能系统架构可能适用于许多医疗设备。例如，当前手动调节以维持足够的患者氧饱和度的气体混合器可以用算法伺服控制，该算法在简单地增加气体输送不合适时安全地检测和报警。此外，混合智能系统架构的效用并不特定于医疗领域。任何需要上下文感知以实现控制算法或模型的最佳性能的用例都可能是潜在的候选者。 混合控制架构有望减少此类系统对人类监督的依赖，如果应用得当，可以减少人为错误，提高成本效益，同时仍然保持质量标准。