Mid-Range Data Acquisition Units UsingGPS and Accelerometers

McDonough, Sean; Danaher, David; Neale, William

doi:10.4271/2018-01-0513

Cited by 7 publications

(1 citation statement)

References 4 publications

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“…[25] Lignin can hardly react with oxygen to form water-soluble fragments until ap henolic hydroxy group in lignin reacts under alkaline conditions to form ap henolate ion. [27] Delignification would quickly slow down once the pH dropped below av alue of approximately7 .Acrucial role of the solid alkali is to neutralize the various acidic species formed during CAOSA.A lthough it is barely soluble, almost all of the MgO is dissolved in the liquor after cooking.…”

Section: Mechanisms In the Caosa Processesmentioning

confidence: 99%

Cooking with Active Oxygen and Solid Alkali: A Promising Alternative Approach for Lignocellulosic Biorefineries

et al. 2017

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Lignocellulosic biomass, a matrix of biopolymers including cellulose, hemicellulose, and lignin, has gathered increasing attention in recent years for the production of chemicals, fuels, and materials through biorefinery processes owing to its renewability and availability. The fractionation of lignocellulose is considered to be the fundamental step to establish an economical and sustainable lignocellulosic biorefinery. In this Minireview, we summarize a newly developed oxygen delignification for lignocellulose fractionation called cooking with active oxygen and solid alkali (CAOSA), which can fractionate lignocellulose into its constituents and maintain its processable form. In the CAOSA approach, environmentally friendly chemicals are applied instead of undesirable chemicals such as strong alkalis and sulfides. Notably, the alkali recovery for this process promises to be relatively simple and does not require causticizing or sintering. These features make the CAOSA process an alternative for both lignocellulose fractionation and biomass pretreatment. The advantages and challenges of CAOSA are also discussed to provide a comprehensive perspective with respect to existing strategies.

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Section: Mechanisms In the Caosa Processesmentioning

confidence: 99%

Cooking with Active Oxygen and Solid Alkali: A Promising Alternative Approach for Lignocellulosic Biorefineries

et al. 2017

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Validation of Telemetry Data Acquisition Using GoPro Cameras

Petroskey¹,

Funk²,

Tibavinsky³

2020

SAE Technical Paper Series

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Evaluating the Accuracy and Reliability of Bicycle GPS Devices

Siddiqui¹,

DiBiase²,

Hoang³

et al. 2021

SAE Int. J. Adv. & Curr. Prac. in Mobility

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<div class="section abstract"><div class="htmlview paragraph">Every day in the United States, an average of approximately two cyclists die in bicycle accidents. Sometimes, forensic engineers are asked to reconstruct these accidents. These cyclists may use bicycle GPS devices to record important data, which they use to monitor their riding activities. By analyzing this data, many questions can be answered regarding various parameters such as speed, position, and riding behavior of the bicyclist.</div><div class="htmlview paragraph">The goal of this study was to analyze the accuracy of the GPS position and speed data. For this study, four different bicycle GPS devices (one of which was paired with a magnetometer Garmin Bicycle Speed Sensor), a GoPro camera equipped with GPS, and a smartphone recording to Strava, were mounted on a bicycle. The bicycle was ridden in an approximately straight line (back and forth), and then around a building, to measure positional and speed accuracy. Additionally, the bicycle was ridden in a straight line, then decelerated to a complete stop for varied durations, and then accelerated forward. This was done to see if the retrieved data showed the bicycle coming to a complete stop, or if the GPS averaged a few data points showing that the rider never came to a stop. Furthermore, the bicycle was stopped at a specific location for a certain duration and the GPS drift of each device was studied. </div><div class="htmlview paragraph">Using VBOX 3i RTK DGPS positional data as the reference, test data revealed that all six bicycle test devices had relative error in positional and speed data. The Garmin Edge 530 had an average speed error of 10.83% and positional error of 21.78 ft. The Garmin Edge 1000 (BSS) had an average speed error of 4.71% and positional error of 15.2 ft. The Lezyne Mega XL had an average speed error of 3.5% and positional error of 4.82 ft. The Wahoo ELEMNT Bolt had an average speed error of 3.53% and a positional error of 10.46 ft. The Strava app had a positional error of 11.45 ft. The GoPro Hero5 had an average speed error of 5.05% and a positional error of 4.79 ft.</div></div>

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Mid-Range Data Acquisition Units UsingGPS and Accelerometers

Cited by 7 publications

References 4 publications

Cooking with Active Oxygen and Solid Alkali: A Promising Alternative Approach for Lignocellulosic Biorefineries

Cooking with Active Oxygen and Solid Alkali: A Promising Alternative Approach for Lignocellulosic Biorefineries

Validation of Telemetry Data Acquisition Using GoPro Cameras

Evaluating the Accuracy and Reliability of Bicycle GPS Devices

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