Amrudin Agovic

MinneTAC Sales Strategies for Supply Chain TAC

We describe two sales strategies used by our agent, MinneTAC, for the 2003 Supply Chain Management Trading Agent Competition (TAC SCM). Both strategies estimate, as the game progresses, the probability of receiving a customer order for different prices and compute the expected profit. We empirically analyze the effect of the discount given by suppliers on orders made the first day of the game, and show that in high-demand games there is a strong correlation between the performance of an agent in the game and the offers it receives from suppliers the first day of the game.

Anomaly detection using manifold embedding and its applications in transportation corridors

The formation of secure transportation corridors, where cargoes and shipments from points of entry can be dispatched safely to highly sensitive and secure locations, is a high national priority. One of the key tasks of the program is the detection of anomalous cargo based on sensor readings in truck weigh stations. Due to the high variability, dimensionality, and/or noise content of sensor data in transportation corridors, appropriate feature representation is crucial to the success of anomaly detection methods in this domain. In this paper, we empirically investigate the usefulness of manifold embedding methods for feature representation in anomaly detection problems in the domain of transportation corridors. We focus on both linear methods, such as multi-dimensional scaling (MDS), as well as nonlinear methods, such as locally linear embedding (LLE) and isometric feature mapping (ISOMAP). Our study indicates that such embedding methods provide a natural mechanism for keeping anomalous points away from the dense/normal regions in the embedding of the data. We illustrate the efficacy of manifold embedding methods for anomaly detection through experiments on simulated data as well as real truck data from weigh stations.

Computer vision issues in the design of a scrub nurse robot

A robot scrub nurse (RSN) is an example of a robotic assistant for surgical environments. Ideally, by taking over management of instruments, it would lower costs of an operation and cut down on mistakes. Of vital importance for such robots is how they interface with the environment. A scrub nurse robot requires the ability to sense the human operators before it can assist. Computer vision offers here a number of advantages over other sensing modalities. In this paper we examine a visual tracking system for a robot scrub nurse. The system works by estimating the hand position and orientation of the main surgeon. This information is needed to guide the robot in delivering instruments directly to the surgeon. Our work outlines the entire visual tracking process and evaluates robustness and accuracy. The end result is a re-implementable and working application, suitable for surgical environments, that also offers a degree of operation robustness.