Identification and Quantification of Cotton Yield Monitor Errors

Cotton yield monitors are an important part of a precision agriculture program and are becoming widely used by cotton producers for making management decisions. Members of the cotton industry have shown interest in using cotton yield monitors for collecting data from production scale variety yield trials (experiments that test yield performance for numerous varieties). Weighing boll buggies are the current industry standard for measuring yield in variety trials. This process is time consuming and requires extra equipment and labor. The ability to use a yield monitor for measuring yield would streamline variety trial harvesting. Recommendations for the Ag Leader cotton yield monitor state that the monitor should be recalibrated when harvesting a new variety. This poses a problem for collecting yield data from a variety trial due to the numerous calibrations that would be required. The primary objective of this research is to evaluate and enhance monitor performance in order to use it for collecting variety trial data. This will be done using different calibration techniques and post-processing models developed using measured gin turnout and environmental variables. Data were collected in 2007 and 2008 at the Milan Research and Education Center in Milan, TN. Monitor weights were compared to boll buggy weights to determine variation between these two yield estimation techniques. This measured variation is defined as Yield Prediction Error (YPE). Before calibration, yield explained 44% of the variation in YPE. After post-calibration, moisture and yield explained 48% of the variation in YPE. Post-processing models were developed using these types of relationships but were unsuccessful as they introduced more variation into the data set. The relationship of YPE to moisture suggests that boll buggy weights should be adjusted to a common moisture content. The relationship of YPE to yield suggests that improvements could be made to the monitor. Post-processing the data using yield in the model was able to reduce the mean absolute error to 2.5% from 3.3% using only calibration C (recalibrating when weather or other events cause a multiple day stoppage in harvesting). Tukey’s mean separation test was used for both yield measurement techniques to determine differences in variety trial results. In both 2007 and 2008, the variety trial results returned the same differences for both yield estimation techniques. This dataset supports that with proper calibration, the yield monitor can be used to collect yield data for cotton variety trials

A three-dimensional, dynamic model of the human body for lifting motions

Lower back pain is prevalent in society and manual lifting has been linked as one potential cause of these types of injuries. Therefore, the 3dLift biomechanical model was developed in this research with the goal of quantitatively analyzing lifting motions. The model divided the body into fifteen segments that were connected by fourteen anatomical joints. During experimental trials, a volunteer subject lifted an object using four different lifting combinations: symmetric leglifts, asymmetric leglifts, symmetric backlifts, and asymmetric backlifts. In order to individualize the 3dLift model, anthropometric parameters were estimated using measurements taken on the subject. During the lifting trials, the subject wore reflective markers placed on anatomical landmarks, the motions of which were tracked by five video cameras. The subject also stood with each foot on a separate force platform that was used to determine ground reaction forces and centers of pressure. Signal processing methods were utilized to predict the marker positions that were obscured during the lifting trials, and digital filtering was implemented to attenuate noise in the data. After reducing the experimental errors, the segment coordinate axes, Cardan angles, joint center positions, and mass center positions were calculated. The changes in the segment orientations with respect to time were then analyzed to determine the three-dimensional kinematics of the segments. Anthropometric, video, and force platform information were combined in equations of motion that were derived to predict the forces and moments occurring at the joints during the lifting motions. A lower body formulation was developed that started with the measured ground reactions at the feet and proceeded through the segments to the T10/T11 intervertebral joint. Similarly, an upper body formulation was derived that began with a known lifted load at the hands and continued through the segments to the same T10/T11 intervertebral joint. While predicting joint forces and moments, the two formulations also served as a means of validating the 3dLift model by comparing the results at the T10/T11 joint. While there is much work yet to be done in this research area, the 3dLift model takes the first steps by developing a systematic methodology for studying lifting motions

It's worse than you thought : the feedback negativity and violations of reward prediction in gambling tasks

The reinforcement learning theory suggests that the feedback negativity should be larger when feedback is unexpected. Two recent studies found, however, that the feedback negativity was unaffected by outcome probability. To further examine this issue, participants in the present studies made reward predictions on each trial of a gambling task where objective reward probability was indicated by a cue. In Study 1, participants made reward predictions following the cue, but prior to their gambling choice; in Study 2, predictions were made following their gambling choice. Predicted and unpredicted outcomes were associated with equivalent feedback negativities in Study 1. In Study 2, however, the feedback negativity was larger for unpredicted outcomes. These data suggest that the magnitude of the feedback negativity is sensitive to violations of reward prediction, but that this effect may depend on the close coupling of prediction and outcome

Speed Management and Speed Reduction in Portland, OR

In 2015, the Portland City Council unanimously passed a resolution committing Portland to Vision Zero, the goal to eliminate traffic deaths and serious injuries. An underpinning of Vision Zero is that streets are managed for safe speeds. This presentation will summarize Portland\u27s speed management process, how it relates to achieving Vision Zero, and present two case studies in which speed limits were reduced: (1) a 25 mi/h to 20 mi/h reduction on residential streets and (2) various reductions on arterials and collectors. Reduction sites in which additional treatments were implemented, such as speed humps and fixed speed safety cameras, will also be discussed. Results of the data analysis will be shared, along with next steps in Portland\u27s speed management process.https://pdxscholar.library.pdx.edu/trec_seminar/1229/thumbnail.jp

Integrating Trust in technology and Computer Self- Efficacy within the Post-Adoption Context: An Empirical Examination

This work in progress examines the roles that Trust in Technology (TRIT) and Computer Self-efficacy (CSE) play in predicting post-adoptive usage behavior. Under the umbrella of social cognitive theory, it uses attribution theory and the trust literature to develop an integrative model of trust and self-efficacy. Specifically, we posit that TRIT impacts users’ CSE and that these beliefs lead to post-adoptive information technology (IT) usage. To examine our model, we propose a study that brings CSE, TRIT, Deep System Usage, and Trying to Innovate with IT into a single articulated model. Using data from 372 students, we use PLS to examine the hypothesized relationships. We conclude with a discussion of findings