We evaluate a handheld multispectral fluorescence imaging device to detect a bacterial colony on leafy greens. The most common diseases causing illness transmitted by leafy vegetables are norovirus, Shiga toxin-producing E. coli (STEC), and Salmonella, according to a CDC. Listeria and Cyclospora can also cause these illnesses. We will test the efficacy of a Contamination, Sanitization Inspection, and Disinfection (CSI-D) system using light at two fluorescence excitation wavelengths, ultraviolet C (UVC) at 275 nm and violet at 405 nm. Tests will evaluate the detection efficacy of device on inoculated control specimens of leafy greens during a time lapsed study.
In this paper, a human-autonomy interaction approach is presented that enables autonomy to proactively dialogue with human teammates to maintain common understanding of the underlying processes. A class of human-autonomy systems where the role of the autonomy is to assist a human teammate in decision making tasks is considered. The autonomy maintains its knowledge of the processes and the environment in a Bayesian engine, and uses a Bayesian inference framework to provide decision support. Any discrepancy in the knowledge of the process between the autonomy and the human teammate may lead to inefficient decision support. The presented curious partner interaction framework uses a dialogue-based approach to resolve differences between the human and the autonomy. The dialog acts as a feedback mechanism to revise the Bayesian engine representation of the autonomy’s knowledge to establish common ground. An application to military operations is considered where a digital assistant uses the curious partner framework to provide decision support to a commander.
While traditional sensors provide accurate measurements of quantifiable information, humans provide better qualitative information and holistic assessments. Sensor fusion approaches that team humans and machines can take advantage of the benefits provided by each while mitigating the shortcomings. These two sensor sources can be fused together using Bayesian fusion, which assumes that there is a method of generating a probabilistic representation of the sensor measurement. This general framework of fusing estimates can also be applied to joint human-machine decision making. In the simple case, binary decisions can be fused by using a probability of taking an action versus inaction from each decision-making source. These are fused together to arrive at a final probability of taking an action, which would be taken if above a specified threshold. In the case of path planning, rather than binary decisions being fused, complex decisions can be fused by allowing the human and machine to interact with each other. For example, the human can draw a suggested path while the machine planning algorithm can refine it to avoid obstacles and remain dynamically feasible. Similarly, the human can revise a suggested path to achieve secondary goals not encoded in the algorithm such as avoiding dangerous areas in the environment.
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