Leveraging Active Learning for Failure Mode Acquisition.

Identifying failure modes is an important task to improve the design and reliability of a product and can also serve as a key input in sensor selection for predictive maintenance. Failure mode acquisition typically relies on experts or simulations which require significant computing resources. With the recent advances in Natural Language Processing (NLP), efforts have been made to automate this process.

Sensor Selection Framework for Designing Fault Diagnostics System.

In a world of rapidly changing technologies, reliance on complex engineered systems has become substantial. Interactions associated with such systems as well as associated manufacturing processes also continue to evolve and grow in complexity. Consider how the complexity of manufacturing processes makes engineered systems vulnerable to cascading and escalating failures; truly a highly complex and evolving system of systems.

Haptic perception of users with low vision and their needs in haptic-incorporated user interfaces.

Purpose: This paper reports on research aimed at advancing understanding haptic capability and needs of users with low vision. The objective is to apply this understanding to the design of haptic-incorporated user interfaces.

Method: Study 1 investigated the haptic perception between sighted participants and those with low vision through the magnitude estimation technique, and Study 2 explored the degree to which similar user interface needs were observed among the two vision groups.