ITR - (ASE + NHS) - (int): Intelligent Human-Machine Interface & Control for Highly Automated Chemical Screening Processes

ITR - (ASE NHS) - (int)：智能人机界面

• 批准号: 0426852
• 负责人: David Kaber
• 金额: $ 79.81万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-10-01 至 2008-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0426852&HistoricalAwards=false
• 关键词: ITR; ASE; NHS; int; Intelligent

High-throughput toxicity screening (testing) of dangerous chemical agents for effects on human cells and cell functions is a rapidly developing international, biotechnology industry. Advanced robots have replaced human operators and manual control of screening processes to promote safe, quick and accurate assessment of chemicals and toxins. In the screening process, the role of the human operator has changed to that of supervisory controller of multiple robots manning multiple process lines and performing simultaneous experiments. The operator's task now includes monitoring robot states, detecting errors, and intervening in process control for failure mode recovery. Operators are under high workload and time stress to properly sequence experiments and to quickly make changes if they do not progress as planned. The information requirements for effective performance have expanded dramatically; task workload is now primarily cognitive vs. physical; and there is a need to achieve high levels of situation awareness in dangerous chemical operations. All of these changes have had a major affect on operator stress and work health.In this project, the PI and his team will develop an intelligent/adaptive, human-machine interface to support the new role of screening process supervisors in safe and effective, distributed control of high time stress and high risk, automated chemical and toxicity testing. Development of this technology will be based on cognitive modeling of supervisory controller behaviors during actual chemical screening processes and model predictions of operator performance with different interactive information display design alternatives during the (model) design phase and during chemical process run-time. The PI will prototype control interfaces and shared situation awareness displays for operators that integrate and display process output data adapted to operator concurrent performance needs and functional (physiological) states. The work will also involve creating protocols for long-distance, remote operation of automated screening processes under varying network communication conditions to provide access to "start-up" companies and developing nations with critical screening needs (e.g., anti-terrorism work). In addition, the intelligent interface content and the remote process control scenario will be informed by, and adapted based on, an automated robot (mechanical) "health" monitoring system. Outcomes of the project will include a prototype novel intelligent, adaptive interface technology as embodied in a remote process control system. Advanced computational tools will also be developed to classify operator functional states in real-time, based on physiological data, and to relate this information to screening task workload and performance. Physiological and performance data models will be used as a basis for structuring the cognitive model to promote highly accurate operator performance predictions and facilitate effective dynamic interface configuration for remote screening process control.Broader Impacts: This research will impact and enhance the safety and effectiveness of high-throughput, chemical agent screening, thanks to a combination of new approaches to distributed network control of complex automated systems, user-centered design of adaptive/intelligent interfaces, and support of complex system operator situation awareness and decision making through process information integration and management based on operator functional states. The work will result in increased access for new companies and developing countries to highly specialized and expensive automated, chemical screening technologies potentially accelerating the development of new biotechnologies. In addition, the research will provide specialized training for graduate students participating in the project and through faculty development of new course modules, related to the project, integrated in existing computer science, electrical engineering, and industrial engineering curriculums.

危险化学制剂对人体细胞和细胞功能影响的高通量毒性筛选（测试）是一个快速发展的国际生物技术产业。 先进的机器人已经取代了人类操作员和人工控制筛选过程，以促进对化学品和毒素的安全、快速和准确评估。 在筛选过程中，人类操作员的角色已经变成了多个机器人的监督控制器，这些机器人配备多条生产线并同时进行实验。 操作员的任务现在包括监控机器人状态、检测错误以及干预过程控制以进行故障模式恢复。 操作员在高工作量和时间压力下正确排序实验，并在未按计划进行时快速进行更改。 有效执行的信息需求急剧扩大;任务工作量现在主要是认知与物理;在危险化学品作业中需要实现高水平的态势感知。 所有这些变化都对操作员的压力和工作健康产生了重大影响。在这个项目中，PI和他的团队将开发一个智能/自适应的人机界面，以支持筛选过程监督员的新角色，安全有效地对高时间压力和高风险进行分布式控制，自动化化学和毒性测试。 这种技术的发展将基于认知建模的监督控制器的行为在实际的化学筛选过程和模型预测的操作员的表现与不同的交互式信息显示设计方案在（模型）设计阶段和在化学过程运行时间。 PI将为操作员提供原型控制界面和共享态势感知显示器，这些界面和显示器集成并显示适应操作员并发性能需求和功能（生理）状态的过程输出数据。 这项工作还将涉及在不同的网络通信条件下创建自动筛选过程的远距离远程操作协议，以提供对“初创”公司和具有关键筛选需求的发展中国家的访问（例如，反恐工作）。 此外，智能界面内容和远程过程控制方案将由自动化机器人（机械）“健康”监控系统通知并根据其进行调整。 该项目的成果将包括一个原型新颖的智能，自适应接口技术，体现在远程过程控制系统。 还将开发先进的计算工具，根据生理数据对操作员的功能状态进行实时分类，并将这些信息与筛选任务的工作量和性能联系起来。 生理和性能数据模型将被用作构建认知模型的基础，以促进高度准确的操作员性能预测，并促进有效的动态界面配置，用于远程筛选过程控制。这项研究将影响和提高高通量，化学剂筛选，由于复杂自动化系统的分布式网络控制的新方法的组合，自适应/智能接口的以用户为中心的设计，以及通过基于操作员功能状态的过程信息集成和管理，支持复杂系统操作员态势感知和决策。 这项工作将使新公司和发展中国家有更多机会获得高度专业化和昂贵的自动化化学筛选技术，从而有可能加速新生物技术的开发。 此外，该研究将为参与该项目的研究生提供专业培训，并通过教师开发与该项目相关的新课程模块，将其整合到现有的计算机科学，电气工程和工业工程课程中。