Estimation of Fuel Quality Using Statistical Regression-Based Analysis of Leidenfrost Droplets

2021 ◽  
Author(s):  
Awan Bhati ◽  
Palash V. Acharya ◽  
Ofodike A. Ezekoye ◽  
Vaibhav Bahadur
Keyword(s):  
Near Infrared ◽  
Polynomial Regression ◽  
Liquid Droplet ◽  
Data Bank ◽  
Training Data ◽  
Transformation Model ◽  
Fuel Quality ◽  
Statistical Regression ◽  
Time Data ◽  
Evaporation Time

Abstract Accurate measurement of fuel quality is critical for automotive applications as it impacts engine performance and emissions. A number of techniques have been proposed to measure fuel quality including acoustic wave speed sensors, chemometric modeling, near-infrared spectrophotometry, Raman spectroscopy etc. All of these techniques are complex in nature and require expensive equipment. In contrast, we propose a novel, simple and rapid method for estimating fuel quality in field environments. This involves measuring the evaporation time of fuel droplets in the Leidenfrost state, and using the results of statistical data analysis conducted on an experimental data bank comprising evaporation time data for similar droplets. The Leidenfrost state refers to a liquid droplet hovering on its own vapor layer on a superheated surface. To showcase our approach, evaporation time was measured for droplets consisting of isopropyl alcohol (IPA) and water blends with varying parameters such as IPA fraction (0-1), droplet volume (20–100μL) and surface temperature (200–340 °C). The resulting data bank (96 data points) was used to train and evaluate the performance of a polynomial regression (using a semilogarithmic transformation) model in predicting the evaporation time as a function of the above-mentioned parameters. R2 accuracies of 97.34% (training data), 96.82% (test data), 97.46% (total) and a relative error within ± 0.25% for the entire dataset was obtained using the regression model, which highlights the predictive capabilities of our approach.

New active learning algorithms for near-infrared spectroscopy in agricultural applications

2021 ◽  
Vol 69 (4) ◽  
pp. 297-306
Author(s):  
Julius Krause ◽  
Maurice Günder ◽  
Daniel Schulz ◽  
Robin Gruna
Keyword(s):  
Active Learning ◽  
Near Infrared ◽  
Agricultural Products ◽  
Training Data ◽  
Calibration Model ◽  
Learning Approaches ◽  
Training Samples ◽  
Agricultural Applications ◽  
Selection Of

Abstract The selection of training data determines the quality of a chemometric calibration model. In order to cover the entire parameter space of known influencing parameters, an experimental design is usually created. Nevertheless, even with a carefully prepared Design of Experiment (DoE), redundant reference analyses are often performed during the analysis of agricultural products. Because the number of possible reference analyses is usually very limited, the presented active learning approaches are intended to provide a tool for better selection of training samples.

scholarly journals AVALIAÇÃO ANALÍTICA DE ANALISADOR DE COMBUSTÍVEL COMERCIAL NO CONTROLE DE QUALIDADE DE ETANOL E GASOLINA COMUM

Química Nova ◽  
2021 ◽  
Author(s):  
Sthefany Sena ◽  
Ernesto Ferreira
Keyword(s):  
Standard Method ◽  
News Media ◽  
Near Infrared ◽  
Bias Error ◽  
Fuel Quality ◽  
Analytical Instruments ◽  
Anomalous Data ◽  
Accuracy And Precision ◽  
Type C ◽  
Standard Solution

ANALYTICAL EVALUATION OF COMMERCIAL FUEL ANALYZER IN GASOLINE AND ETHANOL FUEL QUALITY CONTROL. Brazilian news media is full of examples about fuels out of the compliances and needs to control fuel quality. This work proposed an analytical validation of a commercial near infrared photometer (Xerloq, Tech Chrom Analytical Instruments, Brazil) able to identify percentage of ethanol in fuels comparing with NBR standards. Four procedures were selected: analysis of ethanol percentage in gasoline type C samples comparing with standard method ABNT 13992; analysis of ethanol percentage in fuel ethanol samples comparing with standard method ABNT 5992; linearity correlation and selectivity of standard solution analysis. Comparisons between the photometer and standards measurements were to determine the accuracy and precision of the device which presented a little variation from the norm (ER < 1.3%, sR < 0.4%). With regard to the test for linearity, Ethanol:water mixtures was analyzed with R-squared larger than 0.999 with RMSEV less than 1%. On selectivity tests, Gasoline:Ethanol:water and Methanol:Ethanol:water mixtures were measured with bias error not exceeding 5% for regulated samples. In conclusion, this fuel analyzer can be used to analyze ethanol content in Brazilian fuels with similar results comparing with classical standard volumetric and glass densimeter analysis and may indicate adulteration gasoline and ethanol samples by anomalous data.

scholarly journals Tracking and Visualizing Signs of Degradation for Early Failure Prediction of Rolling Bearings

2021 ◽  
Vol 33 (3) ◽  
pp. 629-642
Author(s):  
Sana Talmoudi ◽  
Tetsuya Kanada ◽  
Yasuhisa Hirata ◽  
◽  
Keyword(s):  
Real Time ◽  
Training Data ◽  
High Dimensional ◽  
Time Data ◽  
Early Failure ◽  
Rolling Bearings ◽  
Full Spectrum ◽  
Time Frequency ◽  
Vibration Data ◽  
Visualization Technology

Predictive maintenance, which means detection of failure ahead of time, is one of the pillars of Industry 4.0. An effective method for this technique is to track early signs of degradation before failure occurs. This paper presents an innovative failure predictive scheme for machines. The proposed scheme combines the use of the full spectrum of vibration data from the machines and a data visualization technology. This scheme requires no training data and can be started quickly after installation. First, we proposed to use the full spectrum (as high-dimensional data vectors) with no cropping and no complex feature extraction and to visualize the data behavior by mapping the high-dimensional vectors into a two-dimensional (2D) map. This ensures simplicity of the process and less possibility of overlooking important information as well as provide a human-friendly and human-understandable output. Second, we developed a real-time data tracker that can predict failure at an appropriate time with sufficient allowance for maintenance by plotting real-time frequency spectrum data of the target machine on a 2D map created from normal data. Finally, we verified our proposal using vibration data of bearings from real-world test-to-failure measurements obtained from the IMS dataset.

Multichannel FMCW lidar for imaging velocimetry and range finding

2021 ◽  
Author(s):  
Iskander Gazizov ◽  
Sergei Zenevich ◽  
Oleg Benderov ◽  
Alexander Rodin
Keyword(s):  
Near Infrared ◽  
Moving Objects ◽  
Single Channel ◽  
Basic Research ◽  
Single Mode ◽  
Single Mode Fiber ◽  
Time Data ◽  
Range Finding ◽  
Scan Time ◽  
Laser Control

&lt;p&gt;We present a concept of near-infrared FMCW lidar for real-time low-resolution imaging velocimetry and range finding of moving objects. One of the problems this instrument to challenge is the detection of unmanned aerial vehicles in an urban environment. The use of a lidar-based system is either in the detection of the object itself or of the wingtip vortices generated by rotating blades. A significant drawback of typical wind lidar is the long measurement time associated with the need to scan the area of &amp;#8203;&amp;#8203;interest, therefore we propose an 8x2 matrix of receivers to reduce the total scan time. The main feature of the instrument is the use of commercially available components, including DFB lasers and single-mode fiber for the optical circuit, which can significantly reduce the cost of the device, as well as development time. Data processing and laser control are handled by the FPGA. The characteristics of the multichannel lidar are estimated based on ongoing testing of the single-channel prototype.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Acknowledgements&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;This work has been supported by the Russian Foundation for Basic Research grants #19-29-06104&lt;/p&gt;

Computer Intelligence in Healthcare

2013 ◽  
pp. 716-715 ◽  
Author(s):  
Pramit Ghosh ◽  
Debotosh Bhattacharjee ◽  
Mita Nasipuri ◽  
Dipak Kumar Basu
Keyword(s):  
Embedded System ◽  
Room Temperature ◽  
Cooling System ◽  
Low Cost ◽  
Environmental Parameters ◽  
Training Data ◽  
Data Sets ◽  
Air Conditioner ◽  
Time Data ◽  
Full Speed

Low cost solutions for the development of intelligent bio-medical devices that not only assist people to live in a better way but also assist physicians for better diagnosis are presented in this chapter. Two such devices are discussed here, which are helpful for prevention and diagnosis of diseases. Statistical analysis reveals that cold and fever are the main culprits for the loss of man-hours throughout the world, and early pathological investigation can reduce the vulnerability of disease and the sick period. To reduce this cold and fever problem a household cooling system controller, which is adaptive and intelligent in nature, is designed. It is able to control the speed of a household cooling fan or an air conditioner based on the real time data, namely room temperature, humidity, and time for which system is active, which are collected from environment. To control the speed in an adaptive and intelligent manner, an associative memory neural network (Kramer) has been used. This embedded system is able to learn from training set; i.e., the user can teach the system about his/her feelings through training data sets. When the system starts up, it allows the fan to run freely at full speed, and after certain interval, it takes the environmental parameters like room temperature, humidity, and time as inputs. After that, the system takes the decision and controls the speed of the fan.

scholarly journals An Algorithm to Retrieve Total Precipitable Water Vapor in the Atmosphere from FengYun 3D Medium Resolution Spectral Imager 2 (FY-3D MERSI-2) Data

2020 ◽  
Vol 12 (21) ◽  
pp. 3469
Author(s):  
Bilawal Abbasi ◽  
Zhihao Qin ◽  
Wenhui Du ◽  
Jinlong Fan ◽  
Chunliang Zhao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Vapor ◽  
Near Infrared ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Optical Remote Sensing ◽  
Statistical Regression ◽  
Sensing Applications ◽  
Medium Resolution ◽  
Spectral Imager

The atmosphere has substantial effects on optical remote sensing imagery of the Earth’s surface from space. These effects come through the functioning of atmospheric particles on the radiometric transfer from the Earth’s surface through the atmosphere to the sensor in space. Precipitable water vapor (PWV), CO2, ozone, and aerosol in the atmosphere are very important among the particles through their functioning. This study presented an algorithm to retrieve total PWV from the Chinese second-generation polar-orbiting meteorological satellite FengYun 3D Medium Resolution Spectral Imager 2 (FY-3D MERSI-2) data, which have three near-infrared (NIR) water vapor absorbing channels, i.e., channel 16, 17, and 18. The algorithm was improved from the radiance ratio technique initially developed for Moderate-Resolution Imaging Spectroradiometer (MODIS) data. MODTRAN 5 was used to simulate the process of radiant transfer from the ground surfaces to the sensor at various atmospheric conditions for estimation of the coefficients of ratio technique, which was achieved through statistical regression analysis between the simulated radiance and transmittance values for FY-3D MERSI-2 NIR channels. The algorithm was then constructed as a linear combination of the three-water vapor absorbing channels of FY-3D MERSI-2. Measurements from two ground-based reference datasets were used to validate the algorithm: the sun photometer measurements of Aerosol Robotic Network (AERONET) and the microwave radiometer measurements of Energy’s Atmospheric Radiation Measurement Program (ARMP). The validation results showed that the algorithm performs very well when compared with the ground-based reference datasets. The estimated PWV values come with root mean square error (RMSE) of 0.28 g/cm2 for the ARMP and 0.26 g/cm2 for the AERONET datasets, with bias of 0.072 g/cm2 and 0.096 g/cm2 for the two reference datasets, respectively. The accuracy of the proposed algorithm revealed a better consistency with ground-based reference datasets. Thus, the proposed algorithm could be used as an alternative to retrieve PWV from FY-3D MERSI-2 data for various remote sensing applications such as agricultural monitoring, climate change, hydrologic cycle, and so on at various regional and global scales.

scholarly journals A Comprehensive Comparative Analysis of the Basic Theory of the Short Term Bus Passenger Flow Prediction

Symmetry ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 369 ◽  
Author(s):  
Huawei Zhai ◽  
Licheng Cui ◽  
Yu Nie ◽  
Xiaowei Xu ◽  
Weishi Zhang
Keyword(s):  
Real Time ◽  
Public Transportation ◽  
Transportation Systems ◽  
Training Data ◽  
Time Data ◽  
Short Term ◽  
Passenger Flow ◽  
Modeling Process ◽  
Automatic Passenger Counters ◽  
Public Transportation Systems

In order to meet the real-time public travel demands, the bus operators need to adjust the timetables in time. Therefore, it is necessary to predict the variations of the short-term passenger flow. Under the help of the advanced public transportation systems, a large amount of real-time data about passenger flow is collected from the automatic passenger counters, automatic fare collection systems, etc. Using these data, different kinds of methods are proposed to predict future variations of the short-term bus passenger flow. Based on the properties and background knowledge, these methods are classified into three categories: linear, nonlinear and combined methods. Their performances are evaluated in detail in the major aspects of the prediction accuracy, the complexity of training data structure and modeling process. For comparison, some long-term prediction methods are also analyzed simply. At last, it points that, with the help of automatic technology, a large amount of data about passenger flow will be collected, and using the big data technology to speed up the data preprocessing and modeling process may be one of the directions worthy of study in the future.

scholarly journals Liquid Droplet Microresonators

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 473 ◽  
Author(s):  
Antonio Giorgini ◽  
Saverio Avino ◽  
Pietro Malara ◽  
Paolo De Natale ◽  
Gianluca Gagliardi
Keyword(s):  
Acoustic Waves ◽  
Near Infrared ◽  
Liquid Droplet ◽  
Scattered Light ◽  
Surface Acoustic Waves ◽  
Optical Quality ◽  
Whispering Gallery Modes ◽  
New Paradigm ◽  
Frequency Locking ◽  
Whispering Gallery

We provide here an overview of passive optical micro-cavities made of droplets in the liquid phase. We focus on resonators that are naturally created and suspended under gravity thanks to interfacial forces, illustrating simple ways to excite whispering-gallery modes in various slow-evaporation liquids using free-space optics. Similar to solid resonators, frequency locking of near-infrared and visible lasers to resonant modes is performed exploiting either phase-sensitive detection of the leakage cavity field or multiple interference between whispering-gallery modes in the scattered light. As opposed to conventional micro-cavity sensors, each droplet acts simultaneously as the sensor and the sample, whereby the internal light can detect dissolved compounds and particles. Optical quality factors up to 107–108 are observed in liquid-polymer droplets through photon lifetime measurements. First attempts in using single water droplets are also reported. These achievements point out their huge potential for direct spectroscopy and bio-chemical sensing in liquid environments. Finally, the first experiments of cavity optomechanics with surface acoustic waves in nanolitre droplets are presented. The possibility to perform studies of viscous-elastic properties points to a new paradigm: a droplet device as an opto-fluid-mechanics laboratory on table-top scale under controlled environmental conditions.

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