Extracting Horizontal Curvature Data from GIS Maps: Clustering Method

This paper presents the use of a clustering method for automatically estimating horizontal curvature data and crash modification factors (CMFs) using Geographic Information System (GIS) roadway shapefiles. The clustering method identifies distinct sections on a roadway, either curved or tangent, based on the proximity of the approximated curvature values of data points from GIS roadway centerline shapefiles, and calculates horizontal curvature data and the corresponding CMFs. The results of the clustering method are compared with two other methods: (1) the mobile access vehicle method based on field GPS measurements and (2) the manual data extraction method based on satellite images. The comparison was conducted on a total of 24.7 mi of four NJ rural two-lane roads. The results showed that the CMFs estimated by the clustering method were within 12.2 and 15.5% of the ones produced by the mobile asset vehicle and the manual data extraction method, respectively. In addition, the sensitivity of the manually extracted horizontal curvature data was examined by conducting three additional independent trials. The average percent difference in the calculated CMFs between trials was 15.5%. This study therefore concludes that the clustering method can produce CMF estimates as accurate as the two other methods method much more efficiently in relation to time and money.

A Method of Speed Control during Over-Ground Walking: Using a Digital Light-Emitting Diode Light Strip

The purpose of this report was to introduce the design of a portable, inexpensive and programmable digital light-emitting diode (LED) system to control overground walking speed. The system includes a custom-made 10 meters digital LED strip and a digital microcontroller. By controlling the duration time of the power supply to each LED unit, a visible running lights signal can provide a visual cue for speed control. To evaluate this design, five subjects were asked to walk overground while following the LED visual cue at five different target speeds. The actual walking speeds were determined using Vicon motion capture system. The results of this evaluation showed a good match between the actual and desired speeds. The average percent difference was 2.51%, measured over 250 walking trials by the subjects. 98% of trials had an percent difference smaller than 6.5%, which is the maximum tolerated error within the literature. The inter-trial reliability for the LED speed control system ranged from 0.85 to 0.88 for faster speeds (1.6 m/s, 1.4 m/s), and slightly lower ranging from 0.74 to 0.79 at slower speeds (1.2 m/s, 1.0 m/s, 0.8 m/s).

Estimating Target Values in Preliminary Design: An Automotive Case Study

Estimation modules have been developed for use with the House of Quality. These estimation modules are used to predict the performance of a proposed design based on the values of the Engineering Characteristics. This, paper describes the development of modules for an automotive case study. Specifically, modules for weight, price, acceleration time, and fuel economy are given. Comparison of estimated values to actual values show an average percent difference of less than 10%.

Predicting Lettuce Canopy Photosynthesis with Statistical and Neural Network Models

An artificial neural network (NN) and a statistical regression model were developed to predict canopy photosynthetic rates (Pn) for `Waldman's Green' leaf lettuce (Latuca sativa L.). All data used to develop and test the models were collected for crop stands grown hydroponically and under controlled-environment conditions. In the NN and regression models, canopy Pn was predicted as a function of three independent variables: shootzone CO2 concentration (600 to 1500 mmol·mol-1), photosynthetic photon flux (PPF) (600 to 1100 μmol·m-2·s-1), and canopy age (10 to 20 days after planting). The models were used to determine the combinations of CO2 and PPF setpoints required each day to maintain maximum canopy Pn. The statistical model (a third-order polynomial) predicted Pn more accurately than the simple NN (a three-layer, fully connected net). Over an 11-day validation period, average percent difference between predicted and actual Pn was 12.3% and 24.6% for the statistical and NN models, respectively. Both models lost considerable accuracy when used to determine relatively long-range Pn predictions (≥6 days into the future).

Powder Diffraction Data for Two Energetic Materials and a Proposed Intensity Figure of Merit

Indexed X-ray powder diffraction data are reported for two energetic materials, oxalylhydroxamic acid and 2-diazo-4, 6-dinitrophenol (DDNP). For these two compounds, powder diffraction data calculated from single-crystal structure determinations are also presented and compared to the experimentally observed powder diffraction data. To evaluate the reliability of the experimentally obtained intensities, an intensity figure of merit, IX(N), based on an average percent difference between observed and calculated intensities for a limited number of strong and moderately-strong lines is proposed as a more useful measure of agreement than R1. For N lines with Icalc > X% relative intensity, IX(N) is defined asThis is compared towhich involves a summation over all lines.

A Mechanistic Model for Two-Phase Vertical Slug Flow in Pipes

This paper presents the formulation of a mechanistic model for slug flow in vertical pipes. The equations required to determine holdup, the relevant velocities and pressure loss are presented. The model is fully deterministic and the pressure drop predictions of the model are compared to experimental field data for oil and gas and gas and water wells. For the 143 data points, the model shows an average percent difference of 4.83 percent, which is felt to be excellent.