scholarly journals Radiometric Calibration of an Inexpensive LED-Based Lidar Sensor

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5215
Author(s):  
Jordan Laughlin ◽  
Preston Hartzell ◽  
Craig Glennie ◽  
Jan W. Kovermann
Keyword(s):  
Short Range ◽  
Low Cost ◽  
Time Of Flight ◽  
Target Range ◽  
Calibration Model ◽  
Radiometric Calibration ◽  
Proposed Model ◽  
Calibration Methods ◽  
Mean Square Errors ◽  
Specular Surfaces

Radiometric calibration of laser-based, topographic lidar sensors that measure range via time of flight or phase difference is well established. However, inexpensive, short-range lidar sensors that utilize non-traditional ranging techniques, such as indirect time of flight, may report radiometric quantities that are not appropriate for existing calibration methods. One such lidar sensor is the TeraRanger Evo 60 m by Terabee, whose reported amplitude measurements do not vary smoothly with the amount of return signal power. We investigate the performance of a new radiometric calibration model, one based on a neural network, applied to the Evo 60 m. The proposed model is found to perform similarly to those applied to traditional lidar sensors, with root mean square errors in predicted target reflectance of no more than ±6% for non-specular surfaces. The radiometric calibration model provides a generic approach that may be applicable to other low-cost lidar sensors that report return signal amplitudes that are not smoothly proportional to target range and reflectance.

scholarly journals Polarization Radiometric Calibration in Laboratory for a Channeled Spectropolarimeter

2020 ◽  
Vol 10 (22) ◽  
pp. 8295
Author(s):  
Wenhe Xing ◽  
Xueping Ju ◽  
Jian Bo ◽  
Changxiang Yan ◽  
Bin Yang ◽  
...  
Keyword(s):  
Optical System ◽  
Measurement Accuracy ◽  
Calibration Method ◽  
Stokes Parameters ◽  
Calibration Coefficient ◽  
Calibration Model ◽  
Radiometric Calibration ◽  
Reconstruction Methods ◽  
Proposed Model ◽  
Alignment Errors

The process of radiometric calibration would be coupled with the polarization properties of an optical system for spectropolarimetry, which would have significant influences on reconstructed Stokes parameters. In this paper, we propose a novel polarization radiometric calibration model that decouples the radiometric calibration coefficient and polarization properties of an optical system. The alignment errors of the polarization module and the variation of the retardations at different fields of view are considered and calibrated independently. According to these calibration results, the input Stokes parameters at different fields of view can be reconstructed accurately through the proposed model. Simulations are performed for the presented calibration and reconstruction methods, which indicate that the measurement accuracy of polarization information is improved compared with the traditional undecoupled calibration method.

scholarly journals Assessment and Calibration of a Low-Cost PM2.5 Sensor Using Machine Learning (HybridLSTM Neural Network): Feasibility Study to Build an Air Quality Monitoring System

Atmosphere ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1306
Author(s):  
Donggeun Park ◽  
Geon-Woo Yoo ◽  
Seong-Ho Park ◽  
Jong-Hyeon Lee
Keyword(s):  
Neural Network ◽  
Air Quality ◽  
Short Term Memory ◽  
State Of The Art ◽  
Low Cost ◽  
Multiple Linear Regression Model ◽  
Calibration Model ◽  
Raw Data ◽  
Proposed Model ◽  
Mlr Model

Commercially available low-cost air quality sensors have low accuracy. The improved accuracy of low-cost PM2.5 sensors allows the use of low-cost sensor systems to reasonably investigate PM2.5 emissions from industrial activities or to accurately estimate individual exposure to PM2.5. In this work, we developed a new PM2.5 calibration model (HybridLSTM) by combining a deep neural network (DNN) optimized in calibration problems and a long short-term memory (LSTM) neural network optimized in time-dependent characteristics to improve the performance of conventional calibration algorithms of low-cost PM sensors. The PM2.5 concentrations, temperature and humidity by low-cost sensors and gravimetric-based PM2.5 measuring instrument were sampled for a sufficiently long time. The proposed model was compared with benchmarks (multiple linear regression model (MLR), DNN model) and low-cost sensor results. The gravimetric measurements were used as reference data to evaluate sensor accuracy. For root-mean-square error (RMSE) for PM2.5 concentrations, the proposed model reduced 41–60% of error when compared with the raw data of low-cost sensors, reduced 30–51% of error when compared with the MLR model and reduced 8–40% of error when compared with the MLR model. R2 of HybridLSTM, DNN, MLR and raw data were 93, 90, 80 and 59%, respectively. HybridLSTM showed the state-of-the-art calibration performance for a low-cost PM sensor. In other words, the proposed ML model has state-of-the-art calibration performance among the tested calibration algorithms.

scholarly journals Assessment and Calibration on Low-cost PM2.5 Sensor using Machin Learning (Hybrid-LSTM Neural Network): Feasibility Study to Build Air Quality Monitoring System

2021 ◽  
Author(s):  
Donggeun Park ◽  
Geon-Woo Yoo ◽  
Seong-Ho Park ◽  
Jong-Hyeon Lee
Keyword(s):  
Neural Network ◽  
Air Quality ◽  
Low Cost ◽  
Multiple Linear Regression Model ◽  
Personal Exposure ◽  
Calibration Model ◽  
Raw Data ◽  
Proposed Model ◽  
Long Time ◽  
New Sensors

Although commercially-available low-cost air quality sensors have low accuracy, the sensor system are being used to collect the data for the regulation of PM2.5 emission caused by industrial activities or to estimate the personal exposure for PM2.5. In this work, to solve the accuracy problem of low-cost PM sensor, we developed a new PM2.5 calibration model by combining the deep neural network (DNN) optimized in calibration problem and a LSTM optimized in time-dependent characteristics. First, two datasets were generated to test the accuracy performance and generalization performance of the PM2.5 calibration machine learning (ML) model. The PM2.5 concentrations, temperature and humidity by low-cost sensor and gravimetric-based PM2.5 measuring instrument were sampled for a sufficiently long time. The proposed model was compared with benchmark (multiple linear regression model) and low-cost sensor results. For root mean square error (RMSE) for PM2.5 concentrations, the proposed model reduced 41-60% of error compared to the raw data of low-cost sensor, and reduced 30-51% of error compared to the benchmark model. R2 of ML model, MLR and raw data were 93, 80 and 59 %. Also, the developed model still showed consistent calibration performance when calibrated with new sensors in different locations. Low-cost sensors combined with ML model not only can improve the calibration performance of benchmark, but also can be applied to the sensor monitoring systems for various epidemiologic investigations and regulatory decisions.

Drug Target Group Prediction with Multiple Drug Networks

2020 ◽  
Vol 23 (4) ◽  
pp. 274-284 ◽  
Author(s):  
Jingang Che ◽  
Lei Chen ◽  
Zi-Han Guo ◽  
Shuaiqun Wang ◽  
Aorigele
Keyword(s):  
Drug Target ◽  
Low Cost ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Support Vector ◽  
Multiple Drug ◽  
Property A ◽  
Multiple Networks ◽  
Proposed Model ◽  
The One

Background: Identification of drug-target interaction is essential in drug discovery. It is beneficial to predict unexpected therapeutic or adverse side effects of drugs. To date, several computational methods have been proposed to predict drug-target interactions because they are prompt and low-cost compared with traditional wet experiments. Methods: In this study, we investigated this problem in a different way. According to KEGG, drugs were classified into several groups based on their target proteins. A multi-label classification model was presented to assign drugs into correct target groups. To make full use of the known drug properties, five networks were constructed, each of which represented drug associations in one property. A powerful network embedding method, Mashup, was adopted to extract drug features from above-mentioned networks, based on which several machine learning algorithms, including RAndom k-labELsets (RAKEL) algorithm, Label Powerset (LP) algorithm and Support Vector Machine (SVM), were used to build the classification model. Results and Conclusion: Tenfold cross-validation yielded the accuracy of 0.839, exact match of 0.816 and hamming loss of 0.037, indicating good performance of the model. The contribution of each network was also analyzed. Furthermore, the network model with multiple networks was found to be superior to the one with a single network and classic model, indicating the superiority of the proposed model.

scholarly journals Low-cost Solutions for Velocimetry in Rotating and Opaque Fluid Experiments using Ultrasonic Time of Flight

2021 ◽  
Author(s):  
Fabian Burmann ◽  
Jerome Noir ◽  
Stefan Beetschen ◽  
Andrew Jackson
Keyword(s):  
Acoustic Waves ◽  
Flow Measurement ◽  
Low Cost ◽  
Time Of Flight ◽  
Zonal Flow ◽  
Gravitational Acceleration ◽  
Complex Flow ◽  
Ultrasonic Time ◽  
Theoretical Predictions ◽  
Complex Flow Structure

AbstractMany common techniques for flow measurement, such as Particle Image Velocimetry (PIV) or Ultrasonic Doppler Velocimetry (UDV), rely on the presence of reflectors in the fluid. These methods fail to operate when e.g centrifugal or gravitational acceleration leads to a rarefaction of scatterers in the fluid, as for instance in rapidly rotating experiments. In this article we present two low-cost implementations for flow measurement based on the transit time (or Time of Flight) of acoustic waves, that do not require the presence of scatterers in the fluid. We compare our two implementations against UDV in a well controlled experiment with a simple oscillating flow and show we can achieve measurements in the sub-centimeter per second velocity range with an accuracy of $\sim 5-10\%$ ∼ 5 − 10 % . We also perform measurements in a rotating experiment with a complex flow structure from which we extract the mean zonal flow, which is in good agreement with theoretical predictions.

scholarly journals Vicarious Radiometric Calibration of Ocean Color Bands for FY-3D/MERSI-II at Lake Qinghai, China

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 139
Author(s):  
Shengli Chen ◽  
Xiaobing Zheng ◽  
Xin Li ◽  
Wei Wei ◽  
Shenda Du ◽  
...  
Keyword(s):  
Infrared Imaging ◽  
Large Deviation ◽  
Ocean Color ◽  
Calibration Method ◽  
Surface Measurement ◽  
Radiometric Calibration ◽  
Lake Qinghai ◽  
Vicarious Calibration ◽  
Calibration Methods ◽  
Spectral Imager

To calibrate the low signal response of the ocean color (OC) bands and test the stability of the Fengyun-3D (FY-3D)/Medium Resolution Spectral Imager II (MERSI-II), an absolute radiometric calibration field test of FY-3D/MERSI-II at the Lake Qinghai Radiometric Calibration Site (RCS) was carried out in August 2018. The lake surface and atmospheric parameters were mainly measured by advanced observation instruments, and the MODerate spectral resolution atmospheric TRANsmittance algorithm and computer model (MODTRAN4.0) was used to simulate the multiple scattering radiance value at the altitude of the sensor. The results showed that the relative deviations between bands 9 and 12 are within 5.0%, while the relative deviations of bands 8, and 13 are 17.1%, and 12.0%, respectively. The precision of the calibration method was verified by calibrating the Aqua/Moderate-resolution Imaging Spectroradiometer (MODIS) and National Polar-orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer (VIIRS), and the deviation of the calibration results was evaluated with the results of the Dunhuang RCS calibration and lunar calibration. The results showed that the relative deviations of NPP/VIIRS were within 7.0%, and the relative deviations of Aqua/MODIS were within 4.1% from 400 nm to 600 nm. The comparisons of three on-orbit calibration methods indicated that band 8 exhibited a large attenuation after launch and the calibration results had good consistency at the other bands except for band 13. The uncertainty value of the whole calibration system was approximately 6.3%, and the uncertainty brought by the field surface measurement reached 5.4%, which might be the main reason for the relatively large deviation of band 13. This study verifies the feasibility of the vicarious calibration method at the Lake Qinghai RCS and provides the basis and reference for the subsequent on-orbit calibration of FY-3D/MERSI-II.

Low-cost system for radiometric calibration of UAV-based multispectral imagery

2020 ◽  
pp. 1-15
Author(s):  
Jorge Tadeu Fim Rosas ◽  
Francisco de Assis de Carvalho Pinto ◽  
Daniel Marçal de Queiroz ◽  
Flora Maria de Melo Villar ◽  
Rodrigo Nogueira Martins ◽  
...  
Keyword(s):  
Low Cost ◽  
Multispectral Imagery ◽  
Radiometric Calibration ◽  
Cost System

scholarly journals A New Combined Adjustment Model for Geolocation Accuracy Improvement of Multiple Sources Optical and SAR Imagery

2021 ◽  
Vol 13 (3) ◽  
pp. 491
Author(s):  
Niangang Jiao ◽  
Feng Wang ◽  
Hongjian You
Keyword(s):  
Remote Sensing Data ◽  
Observation Data ◽  
Multiple Sources ◽  
Accuracy Improvement ◽  
Geometric Calibration ◽  
Proposed Model ◽  
Calibration Methods ◽  
Earth Observation Data ◽  
Sar Imagery ◽  
Geolocation Accuracy

Numerous earth observation data obtained from different platforms have been widely used in various fields, and geometric calibration is a fundamental step for these applications. Traditional calibration methods are developed based on the rational function model (RFM), which is produced by image vendors as a substitution of the rigorous sensor model (RSM). Generally, the fitting accuracy of the RFM is much higher than 1 pixel, whereas the result decreases to several pixels in mountainous areas, especially for Synthetic Aperture Radar (SAR) imagery. Therefore, this paper proposes a new combined adjustment for geolocation accuracy improvement of multiple sources satellite SAR and optical imagery. Tie points are extracted based on a robust image matching algorithm, and relationships between the parameters of the range-doppler (RD) model and the RFM are developed by transformed into the same Geodetic Coordinate systems. At the same time, a heterogeneous weight strategy is designed for better convergence. Experimental results indicate that our proposed model can achieve much higher geolocation accuracy with approximately 2.60 pixels in the X direction and 3.50 pixels in the Y direction. Compared with traditional methods developed based on RFM, our proposed model provides a new way for synergistic use of multiple sources remote sensing data.

scholarly journals Improving Air Pollutant Metal Oxide Sensor Quantification Practices through: An Exploration of Sensor Signal Normalization, Multi-Sensor and Universal Calibration Model Generation, and Physical Factors Such as Co-Location Duration and Sensor Age

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 645
Author(s):  
Kristen Okorn ◽  
Michael Hannigan
Keyword(s):  
Metal Oxide ◽  
Low Cost ◽  
Air Pollutant ◽  
Sensor System ◽  
Physical Factors ◽  
Time Averaging ◽  
Calibration Model ◽  
Sensor Signal ◽  
Metal Oxide Sensors ◽  
Signal Normalization

As low-cost sensors have become ubiquitous in air quality measurements, there is a need for more efficient calibration and quantification practices. Here, we deploy stationary low-cost monitors in Colorado and Southern California near oil and gas facilities, focusing our analysis on methane and ozone concentration measurement using metal oxide sensors. In comparing different sensor signal normalization techniques, we propose a z-scoring standardization approach to normalize all sensor signals, making our calibration results more easily transferable among sensor packages. We also attempt several different physical co-location schemes, and explore several calibration models in which only one sensor system needs to be co-located with a reference instrument, and can be used to calibrate the rest of the fleet of sensor systems. This approach greatly reduces the time and effort involved in field normalization without compromising goodness of fit of the calibration model to a significant extent. We also explore other factors affecting the performance of the sensor system quantification method, including the use of different reference instruments, duration of co-location, time averaging, transferability between different physical environments, and the age of metal oxide sensors. Our focus on methane and stationary monitors, in addition to the z-scoring standardization approach, has broad applications in low-cost sensor calibration and utility.

scholarly journals Tower Crane Location Optimization for Heavy Unit Lifting in High-Rise Modular Construction

Buildings ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 121
Author(s):  
Hosang Hyun ◽  
Moonseo Park ◽  
Dowan Lee ◽  
Jeonghoon Lee
Keyword(s):  
Low Cost ◽  
Modular Construction ◽  
Algorithm Optimization ◽  
Location Selection ◽  
Project Completion ◽  
High Rise ◽  
Tower Crane ◽  
Proposed Model ◽  
Study Results ◽  
Crane Selection

Modular construction, which involves unit production in factories and on-site work, has benefits such as low cost, high quality, and short duration, resulting from the controlled factory environment utilized. An efficient tower crane lifting plan ensures successful high-rise modular project completion. For improved efficiency, the lifting plan should minimize the reaching distance of the tower crane, because this distance directly affects the tower crane capacity, which is directly related to crane operation cost. In situations where units are lifted from trailers, the trailer-to-tower crane distance can have a significant impact on the tower crane operation efficiency. However, optimization of this distance to improve efficiency has not been sufficiently considered. This research proposes a genetic algorithm optimization model that suggests optimized tower crane and trailer locations. The case study results show that through the proposed model, the project manager can reflect the optimal location selection and optimal tower crane selection options with minimal cost.

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