We used archival data to examine the predictive validity of a pre-release violence risk assessment battery over six years at a forensic hospital (N=230, 100% male, 63.0% African-American, 34.3% Caucasian). Examining “real world” forensic decision-making is important for illuminating potential areas for improvement. The battery included the Historical-Clinical-Risk Management-20, Psychopathy Checklist-Revised, Schedule of Imagined Violence, and Novaco Anger Scale and Provocation Inventory. Three outcome “recidivism” variables included contact violence, contact & threatened violence, and any reason for hospital return. Results indicated measures of general violence risk and psychopathy were highly correlated but weakly associated with reports of imagined violence and a measure of anger. Measures of imagined violence and anger were correlated with one another. Receiver Operating Characteristic curve analyses revealed, unexpectedly, that none of the scales or subscales predicted recidivism better than chance. Multiple regression indicated the battery failed to account for recidivism outcomes. We conclude by discussing three possible explanations, including timing of assessments, controlled versus field studies, and recidivism base rates.
for Unmanned Aerial Vehicles.
Towards enabling a UAV to autonomously sense and avoid moving obstacles, this thesis makes the following contributions. Initially, an image-based reactive motion planner is developed for a quadrotor to avoid a fast approaching obstacle. Furthermore, A Dubin’s curve based geometry method is developed as a global path planner for a fixed-wing UAV to avoid collisions with aircraft. The image-based method is unable to produce an optimal path and the geometry method uses a simplified UAV model. To compensate
these two disadvantages, a series of algorithms built upon the Closed-Loop Rapid Exploratory Random Tree are developed as global path planners to generate collision avoidance paths in real time. The algorithms are validated in Software-In-the-Loop (SITL) and Hardware-In-the-Loop (HIL) simulations using a fixed-wing UAV model and in real flight experiments using quadrotors. It is observed that the algorithm enables a UAV to avoid moving obstacles approaching to it with different directions and speeds.