Annite stability revised. 1. Hydrogen-sensor data for the reaction annite = sanidine + magnetite + H2

1994 ◽  
Vol 117 (3) ◽  
pp. 229-240 ◽  
Author(s):  
E. Dachs
Keyword(s):  
Hydrogen Sensor ◽  
Sensor Data

Auditory visualization of landmine detector sensor data

2009 ◽  
Author(s):  
Bradley M. Davis ◽  
Woodrow W. Winchester ◽  
Jason D. Zedlitz
Keyword(s):  
Sensor Data

Utilization of electrical charges in the pores of tofu waste for hydrogen production in water

2020 ◽  
pp. 124-135
Author(s):  
I. N. G. Wardana ◽  
N. Willy Satrio
Keyword(s):  
Magnetic Field ◽  
Hydrogen Production ◽  
Sodium Bicarbonate ◽  
Positive Charge ◽  
Electrical Energy ◽  
Hydrogen Sensor ◽  
Water Molecules ◽  
Partial Charge ◽  
Delocalized Electron ◽  
Water Hydrogen

Tofu is main food in Indonesia and its waste generally pollutes the waters. This study aims to change the waste into energy by utilizing the electric charge in the pores of tofu waste to produce hydrogen in water. The tofu pore is negatively charged and the surface surrounding the pore has a positive charge. The positive and negative electric charges stretch water molecules that have a partial charge. With the addition of a 12V electrical energy during electrolysis, water breaks down into hydrogen. The test was conducted on pre-treated tofu waste suspension using oxalic acid. The hydrogen concentration was measured by a MQ-8 hydrogen sensor. The result shows that the addition of turmeric together with sodium bicarbonate to tofu waste in water, hydrogen production increased more than four times. This is due to the fact that magnetic field generated by delocalized electron in aromatic ring in turmeric energizes all electrons in the pores of tofu waste, in the sodium bicarbonate, and in water that boosts hydrogen production. At the same time the stronger partial charge in natrium bicarbonate shields the hydrogen proton from strong attraction of tofu pores. These two combined effect are very powerful for larger hydrogen production in water by tofu waste.

Anomaly Detection in Sensor Data

2018 ◽  
Vol 18 (1) ◽  
pp. 20-32 ◽  
Author(s):  
Jong-Min Kim ◽  
Jaiwook Baik
Keyword(s):  
Anomaly Detection ◽  
Sensor Data

Study on Fault Diagnosis of Planetary Gearbox in Unmanned Aerial Vehicle Using Multi sensor Data

2020 ◽  
Vol 20 (4) ◽  
pp. 332-342
Author(s):  
Hyung Jun Park ◽  
Seong Hee Cho ◽  
Kyung-Hwan Jang ◽  
Jin-Woon Seol ◽  
Byung-Gi Kwon ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Unmanned Aerial Vehicle ◽  
Sensor Data ◽  
Planetary Gearbox ◽  
Aerial Vehicle

Development and Evaluation of a Pt/Ti-FET-Type Hydrogen Sensor

2010 ◽  
Vol 130 (8) ◽  
pp. 407-411
Author(s):  
Masatoshi Kariya ◽  
Kouhei Mizuhara ◽  
Tomiharu Yamaguchi ◽  
Toshihiko Kiwa ◽  
Shinsuke Kunitsugu ◽  
...  
Keyword(s):  
Hydrogen Sensor

Hydrogen Selectivity of a Proton-Pumping Gate FET Hydrogen Sensor in an AC Modulation Mode

2009 ◽  
Vol 129 (4) ◽  
pp. 110-114 ◽  
Author(s):  
Tomiharu Yamaguchi ◽  
Masanori Takisawa ◽  
Toshihiko Kiwa ◽  
Hironobu Yamada ◽  
Keiji Tsukada
Keyword(s):  
Hydrogen Sensor ◽  
Proton Pumping ◽  
Modulation Mode ◽  
Hydrogen Selectivity

Reduction of Wind Disturbance by Optimizing the Drive Current of Pt Ultra-thin Film Hydrogen Sensor

2020 ◽  
Vol 140 (4) ◽  
pp. 92-96
Author(s):  
Yuto Goda ◽  
Hiroto Shobu ◽  
Kenji Sakai ◽  
Toshihiko Kiwa ◽  
Kenji Kondo ◽  
...  
Keyword(s):  
Thin Film ◽  
Hydrogen Sensor ◽  
Wind Disturbance ◽  
Drive Current ◽  
Ultra Thin Film

A practical approach on non-regular sampling and universal demosaicing of raw image sensor data

2020 ◽  
Vol 2020 (1) ◽  
pp. 91-95
Author(s):  
Philipp Backes ◽  
Jan Fröhlich
Keyword(s):  
Image Quality ◽  
Source Code ◽  
Image Sensor ◽  
Sensor Data ◽  
Color Filter ◽  
Lowpass Filter ◽  
Sampling Errors ◽  
Regular Sampling ◽  
Single Sensor ◽  
Similar Image Quality

Non-regular sampling is a well-known method to avoid aliasing in digital images. However, the vast majority of single sensor cameras use regular organized color filter arrays (CFAs), that require an optical-lowpass filter (OLPF) and sophisticated demosaicing algorithms to suppress sampling errors. In this paper a variety of non-regular sampling patterns are evaluated, and a new universal demosaicing algorithm based on the frequency selective reconstruction is presented. By simulating such sensors it is shown that images acquired with non-regular CFAs and no OLPF can lead to a similar image quality compared to their filtered and regular sampled counterparts. The MATLAB source code and results are available at: http://github. com/PhilippBackes/dFSR

Development of Failure Detection Algorithm for a Slope Using Sensor Data

2018 ◽  
Vol 6 (1) ◽  
pp. 93-101
Author(s):  
Hyun Ki Kim ◽  
Chan Yong Choi ◽  
Ho Suk Bae
Keyword(s):  
Failure Detection ◽  
Detection Algorithm ◽  
Sensor Data
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