Mohan M. Trivedi - US grants

9417435 Jain ABSTRACT UCSD MS Program in World Class Manufacturing Engineering The University of California, San Diego proposes to create a 2-year MS Program World Class Manufacturing Engineering with a strong international and information systems focus. In partnerships with ALCOA Electronic Packaging, Hewlett-Packard, Hughes Aircraft, and TITAN Linkabit, it is targeted toward displaced defense engineers, particularly women and minorities. Aiming to take advantage of manufacturing and management strengths overseas, the program has a unique emphasis on foreign language and culture learning (approximately 50% of the students) and includes a 9-month internship in a manufacturing firm in the U.S. or abroad. With good support from institutions with strong international studies program, it expects a steady state of 30 students per year with expansion in later years to feed in undergraduates. ***

Novel health monitoring strategies for Highway Bridges and Constructed Facilities are of
primary significance to the vitality of our economy. Using latest enabling technologies, the objectives of health monitoring are to detect and assess the level of damage to the civil infrastructure due to severe loading events (caused by natural loads or man-made events) and/or progressive environmental deterioration. Damage identification is performed based on changes in salient response features of the structure, as measured by deployed sensor arrays. Due to the challenging nature of the technical problems associated with this topic, substantial research efforts during the past thirty years were undertaken by many researchers in many areas related to this broad interdisciplinary topic. The proposed research will build on these developments, and address a number of fundamental and basic research challenges towards a next-generation, versatile, efficient, and practical health monitoring strategy. In such a strategy, data from thousands of sensors will be analyzed with long-term and real-time assessment decisionmaking implications. A flexible and scalable software architecture/framework will be developed to integrate real-time heterogeneous sensor data, database and archiving systems, computer vision, data analysis and interpretation, numerical simulation of complex structural systems, visualization, probabilistic risk analysis, and rational statistical decision making procedures. This development will be undertaken in a concerted and focused comprehensive approach by an inter-disciplinary team of Computer Scientists (CS) and Structural Engineers (SE). It is believed that this inter-disciplinary
approach will synergize the resolution of basic technical challenges and allow development of the
framework for future applications in this field. The new framework will also speed up the discovery of new knowledge related to the progressive or sudden deterioration of civil infrastructure systems and the corresponding damage mechanisms. The planned research activities will not only culminate in the deployment of a robust, field-implementable monitoring system, but it will also advance the research frontiers in several active, cutting-edge research areas involving grid storage (curated databases, filesystems, database systems), knowledge-based data integration and advanced query processing, information extraction (data mining, modeling, analysis and visualization), knowledge extraction (reliability/risk analysis, structural health assessment, physics-based model development), and decision support systems (e.g., emergency response, preventive maintenance, rehabilitation).

The entire project will be developed around actual Bridge Testbeds in cooperation with the
California Department of Transportation (Caltrans), and Industry Partners. These Testbeds will be
densely instrumented and continuously monitored, and the recorded response databases will be made available for maximum possible use by interested researchers and engineers worldwide. The actual recorded data streams from both laboratory models and bridge testbeds will be a major component for all phases of this research effort. An Internet Portal will integrate all elements and act as a Gateway for the Project.

The proposed 5 year project duration will allow the opportunity for resolving key basic research issues of relevance to Structural Health Monitoring, and collaboration between CS and SE
is simply a necessity. State-of-the-art data acquisition, transmission, and management, involvement of computer vision, refinement of nonlinear system identification and modeling, and practical
implementation constitute the basic research framework. Applications include long-term condition
assessment and emergency response after natural or man-made disasters and acts of terrorism for all
types of large constructed facilities. From a broader perspective, the proposed effort will be a major boost in defining and shaping additional long-term interaction and collaboration opportunities between CS and SE, with wide national and international implications, as well as strongly benefiting from leveraging resources and ongoing monitoring activities.

The long-term goals of this project are to radically transform the ability of organizations that respond to man-made and natural disasters to gather, process, manage, use and disseminate information both within the emergency response agencies and to the general public. The project explores a multidisciplinary approach consisting of two interrelated research thrusts:
Scalable and robust information technology solutions to facilitate access to the right information, by the right individuals and organizations, at the right time, and
Social science research that investigates the distinctive nature of dynamic virtual organizations, and the social and cultural aspects of information sharing across organizations and individuals.

Research challenges addressed include mechanisms to: enable crisis responders to become rich sources of vital situational information; seamlessly collect data from heterogeneous sources; translate low-level noisy data into meaningful information that can be effectively used for damage assessment and situation awareness; facilitate information sharing and collective decision-making across emergent virtual organizations; and rapidly disseminate information in the form most useful to recipients. Close collaborations with multiple government agencies have been developed to test and validate research in live environments.

The project is expected to result in robust information systems that enable first responders to make well-informed and better decisions, to prioritize their response, and to focus on activities that have the highest potential to save lives and property. The resulting timely and effective response can contain or prevent secondary disasters, and reduce the resulting economic losses and social disruption during disasters.

The project will create new shared data sets for text, video and data mining. This will allow a larger scientific community to test algorithmic innovations against these field gathered data sets. Our community outreach programs will help generate greater awareness of the role of IT, stimulating new innovations as first responders interact more closely with researchers. Our educational programs will generate a better trained crisis management work force.

This project utilizes a holistic, human-centered approach in the design of intelligent driver support systems, where situational criticality estimates are continuously monitored. This requires computational models for accurate estimation of how a driver perceives situations, plans actions, and reacts and interacts with the vehicle and its surround. The specific goal of the project is to develop computational frameworks to analyze attention shifts, using multimodal cues, in an environment where time and safety constraints are critical. Specific research objectives are: (1) Identification of body related indicators of attention switching. This involves utilizing statistical machine-learning algorithms to analyze previously collected ethnographic datasets and determine most useful indicators of attention shifts, including head and eye gaze, hands, feet, and other body motions. (2) Understanding the effect of external visual and audio saliency cues in the driving environment on attention shifts. This involves the analysis of how those multimodal cues affect attention shifts in time- and safety-critical situations, incorporating ?top-down? goal-oriented and ?bottom-up? distraction-based mechanisms. (3) Developing a hierarchical Bayesian model and computational framework for describing the relationship between body cues, external saliency, and driving task, in order to accurately estimate attention and attention shifts. In summary, the project provides a feasibility assessment of detecting how and why attention shifts occur in the vehicular environment with a multimodal sensor suite. Project findings will influence design of active safety systems to reduce crash risk on the roads.