Xi Sigma Pi

The Department of Forestry at Iowa State University provides students with countless opportunities, acknowledgments, and awards. The opportunity to gain membership into the national forestry honor society, xi sigma pi, is one such benefit

Subnanosecond pulse measurements of 10.6 μm radiation with tellurium

Subnanosecond infrared pulses have been measured by noncollinear secondharmonic generation in tellurium. The method is very practical because due to the high refractive index the fine tuning of the phase matching is easily obtained by rotating the crystal around the optic axis

Intramode and Fermi relaxation in CO2, their influence on multiple-pass, short-pulse energy extraction

Analytical, experimental and numerical results concerning the influence of intramode and Fermi relaxation on multiple-pass, nanosecond-pulse energy extraction are presented. Multiple-pass energy extraction experiments show satisfactory agreement with the analytical and numerical calculations which predict a significant increase in extracted energy. In three passes, an amount of 9.7 J/l was extracted at an efficiency of 4.3%, These values are taken with respect to the volume of the beam inside the amplifier. In a single pass only 3.5 J/l was extracted

Fermi and intramode relaxation phenomena in CO2lasers

Using a 1 ns pulse from a short-pulse CO2laser system, the evolution of the gain in a TEA system was studied during and after amplification. This resulted in a very direct observation of a few relaxation processes. We estimated the effective intramode relaxation rate constant to be larger than6 times 10^{6}torr-1/s. The Fermi relaxation time constant was found to be 30 ± 7 ns at 760 torr. We conclude that for nanosecond pulse amplification, intramode relaxation cannot be neglected

Thermodynamic analysis of the thermal and exergetic performance of a mixed gas-steam aero derivative gas turbine engine for power generation

A thermodynamic analysis is performed for an aero derivative gas turbine engine which utilizes steam injection to increase its efficiency. The target was to explore the performance of a high efficiency gas turbine unit for electric power generation without downstream Rankine cycle. A Rankine cycle for exhaust heat recovery is unattractive because of its large response time and cost of investment. The main purpose of this research was to develop a better understanding of how the optimal cycle efficiency is reached, when the steam for injection is generated by use of the turbine exhaust heat. The STIG cycle becomes attractive for grid stabilization because of its low CAPEX and small footprint and response time. A thermodynamic model has been developed to simulate the simple cycle gas turbine, steam generation and effects of steam injection. Reference input parameters for the model are taken for the GE LM6000 turbine as publicly available. The performance of the engine without steam injection as predicted by the model is compared with literature for validation and compares well. The performance of the STIG cycle as a function of operation parameter steam mass flow and design parameters pressure ratio and turbine inlet temperature is investigated and the optimal parameter settings determined. It is found that this type of cycle shows a very specific parameter setting for optimal efficiency. By using steam injection for the chosen turbine and its parameters an efficiency gain of around 11% points and an output power augmentation of 45% can be achieved.</p

Segmentation-guided Domain Adaptation for Efficient Depth Completion

Complete depth information and efficient estimators have become vital ingredients in scene understanding for automated driving tasks. A major problem for LiDAR-based depth completion is the inefficient utilization of convolutions due to the lack of coherent information as provided by the sparse nature of uncorrelated LiDAR point clouds, which often leads to complex and resource-demanding networks. The problem is reinforced by the expensive aquisition of depth data for supervised training. In this work, we propose an efficient depth completion model based on a vgg05-like CNN architecture and propose a semi-supervised domain adaptation approach to transfer knowledge from synthetic to real world data to improve data-efficiency and reduce the need for a large database. In order to boost spatial coherence, we guide the learning process using segmentations as additional source of information. The efficiency and accuracy of our approach is evaluated on the KITTI dataset. Our approach improves on previous efficient and low parameter state of the art approaches while having a noticeably lower computational footprint

Multivariate Confidence Calibration for Object Detection

Unbiased confidence estimates of neural networks are crucial especially for safety-critical applications. Many methods have been developed to calibrate biased confidence estimates. Though there is a variety of methods for classification, the field of object detection has not been addressed yet. Therefore, we present a novel framework to measure and calibrate biased (or miscalibrated) confidence estimates of object detection methods. The main difference to related work in the field of classifier calibration is that we also use additional information of the regression output of an object detector for calibration. Our approach allows, for the first time, to obtain calibrated confidence estimates with respect to image location and box scale. In addition, we propose a new measure to evaluate miscalibration of object detectors. Finally, we show that our developed methods outperform state-of-the-art calibration models for the task of object detection and provides reliable confidence estimates across different locations and scales.Comment: Accepted on CVPR 2020 Workshop: "2nd Workshop on Safe Artificial Intelligence for Automated Driving (SAIAD)