Figure - uploaded by S. M. Babichenko
Content may be subject to copyright.
It is useful to apply a qualitative measure of the coefficient of determination to pre-set the similarity threshold in the LIF analysis. This measure is also known as the Chaddock scale. 

It is useful to apply a qualitative measure of the coefficient of determination to pre-set the similarity threshold in the LIF analysis. This measure is also known as the Chaddock scale. 

Source publication
Article
Full-text available
Remote-sensing approaches for environmental protection and exploration have evolved rapidly in the last decade. Among the new operational tools, hyperspectral Fluorescent LiDAR System FLS® lidar has demonstrated a high sensitivity and the ability to function in complex environments for real-time, robust oil-spill monitoring on airborne or ship-born...

Contexts in source publication

Context 1
... similarity value is calculated with the conventional coefficient of determination, R 2 , which is a statistical mea- sure of how well the model function approximates the actual data points. This proximity measure allows qualitative assessment of the comparison value (e.g. the Chaddock scale, see Table 1) and also calibration of the sensitivity of the analysis. The sensitivity is adjusted based on the forecasted classification error rate, with an R 2 threshold of δ ∈ (0, 1). ...
Context 2
... the similarity value is 0.9233 for the model spectrum with a clean water matrix. Because both values fit the definition of 'very high' (Table 1), the correct model could not be explicitly selected based on the R 2 values, and qualitative assessment must therefore be used. The selection of the correct answer is further complicated upon consideration of the next model with a different oil product, Calsol. ...

Citations

... The combination of SNV and SG second derivation proved to be the most efficient spectral pre-processing procedure, followed by further modelling. Applying the coefficients of determination and their qualitative characteristics known as the Chaddock scale [31], the majority of R 2 values indicated strong relationships (R 2 of 0.7-0.9) between the observed input vs. predicted output wine parameters. Outside this range were the R 2 values for the prediction of wine volatile acidity (VA) and the colour parameter b*, for which the relationship between the observed input and predicted output parameters was not strong, whereas reduced sugars (RS) and colour parameter hue showed a significant but moderate relationship (R 2 ≥ 0.3-0.5). ...
Article
Full-text available
This study investigates the colour and standard chemical composition of must and wines produced from the grapes from Vitis vinifera L., ‘Maraština’, harvested from 10 vineyards located in two different viticultural subregions of the Adriatic region of Croatia: Northern Dalmatia and Central and Southern Dalmatia. The aim was to explore the use of NIR spectroscopy combined with chemometrics to determine the characteristics of Maraština wines and to develop calibration models relating NIR spectra and physicochemical/colour data. Differences in the colour parameters (L*, a*, hue) of wines related to the subregions were confirmed. Colour difference (ΔE) of must vs. wine significantly differed for the samples from the Maraština grapes grown in both subregions. Principal component regression was used to construct the calibration models based on NIR spectra and standard physicochemical and colour data showing high prediction ability of the 13 studied parameters of must and/or wine (average R2 of 0.98 and RPD value of 6.8). Principal component analysis revealed qualitative differences of must and wines produced from the same grape variety but grown in different subregions.
... Comparing the prediction accuracy of the MLP model with the linear model, it is obvious that it has higher, i.e., high prediction accuracy, but it is also characterized by incomparably higher complexity compared to the linear model. The Chaddock scale, presented in Table 10 [51], was used to qualitatively evaluate the coefficient of determination R 2 . It can be seen that the accuracy performance of the MLP model is in the range of 0.7-0.9 and qualifies as high. ...
Article
Full-text available
The main goal of this paper is to create an adaptive model based on multilayer perceptron (MLP) for prediction of average downlink (DL) data throughput per user and average DL data throughput per cell within an LTE network technology and in a geo-space that includes a segment of the Motorway 9th January with the access roads. The accuracy of model prediction is estimated based on relative error (RE). With multiple trainings and testing of 30 different variants of the MLP model, with different metaparameters the final model was chosen whose average accuracy for the Cell Downlink Average Throughput variable is 89.6% (RE = 0.104), while for the Average User Downlink Throughput variable the average accuracy is 88% (RE = 0.120). If the coefficient of determination is observed, the results showed that the accuracy of the best selected prediction model for the first variable is 1.4% higher than the accuracy of the prediction of the selected model for the second dependent variable. In addition, the results showed that the performance of the MLP model expressed over R2 was significantly better compared to the reference multiple linear regression (MLR) model used.
... Several MLAs, such as support vector machine, principal component analysis, decision tree, neural network and wavelet transform, have been used for extraction of relevant spectra features and reduction of high-dimensional spectra data to lower dimensions and thereby reducing computational complexity and increasing classification accuracy (Sobolev and Babichenko, 2013;Gabbarini et al., 2019). Genetic algorithm (an evolutionary algorithm), which has higher classification accuracy than SVM, principal component analysis, Fisher's linear discriminant and forward feature selection, has also been reported (Nyhavn et al., 2011). ...
Article
Full-text available
Viruses remain a significant public health concern worldwide. Recently, humanity has faced deadly viral infections, including Zika, Ebola and the current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The threat is associated with the ability of the viruses to mutate frequently and adapt to different hosts. Thus, there is the need for robust detection and classification of emerging virus strains to ensure that humanity is prepared in terms of vaccine and drug developments. A point or stand-off biosensor that can detect and classify viruses from indoor and outdoor environments would be suited for viral surveillance. Light detection and ranging (LiDAR) is a facile and versatile tool that has been explored for stand-off detection in different environments including atmospheric, oceans and forest sensing. Notably, laser-induced fluorescence-light detection and ranging (LIF-LiDAR) has been used to identify MS2 bacteriophage on artificially contaminated surgical equipment or released amidst other primary biological aerosol particles in laboratory-like close chamber. It has also been shown to distinguish between different picornaviruses. Currently, the potentials of the LIF-LiDAR technology for real-time stand-off surveillance of pathogenic viruses in indoor and outdoor environments have not been assessed. Considering the increasing applications of LIF-LiDAR for potential microbial pathogens detection and classification, and the need for more robust tools for viral surveillance at safe distance, we critically evaluate the prospects and challenges of LIFLiDAR technology for real-time stand-off detection and classification of potentially pathogenic viruses in various environments.
... Correlation analysis was performed to identify weather factors that have a significant impact on the formation of winter wheat yield. Among all the studied indices, an average (noticeable) and high linear correlation dependence was established for 18 indices according to the Chaddock scale (Sobolev, & Babichenko, 2013) (Table 2). Given that the indices of amount of precipitation, air temperature and relative air humidity are components in the calculation of humidification coefficient (HC) and hydrothermal coefficient (HTC), further evaluation excluded the following indices: mean and average of the maximum air temperatures in July; amount of precipitation in August, mean and average of the maximum and minimum air temperatures in August, mean relative air humidity in August; amount of precipitation in April; average of the minimum and maximum air temperatures in May, and HC in November, which have less impact on winter wheat yield compared with the complex index for the month. ...
Article
Full-text available
Wheat is one of the leading agricultural crops grown in all countries of the world and is a major source of calories and nutrients for millions of people. Ukraine is one of the world's leading winter wheat grain producers being one of the top ten producer countries. However, in terms of crop yields, Ukraine is far behind most developed countries. The main limiting factor in winter wheat yield increase is stressful weather conditions during the growing season, especially in the Southern Steppe, characterized by significant aridity of the territory. Timely forecast of winter wheat yield at the regional level is a key element in ensuring food security of the state. The article evaluates the expediency of forecasting winter wheat yield depending on the effect of certain environmental factors. The statistical data on winter wheat yield sown after five predecessor crops using the same cultivation technology and meteorological data of Melitopol meteorological station for 2010-2019 were used for the analysis. Based on the correlation analysis, a number of factors had a significant effect on crop yield, both direct and inverse. It was determined that during the pre-sowing period the conditions of humectation in July (r=0.82) and August (r=-0.76) had the greatest influence on future yield formation. In the autumn period of growing season, the amount of precipitation in November had a significant effect on the increase of the yield (r=0.67). During the winter and restoration of spring vegetation, the formation of the crop was positively influenced by air temperatures in February (r=0.54) and March (r=0.53). High temperatures in May had a significant negative correlation (r=-0.69) on plant productivity. Based on the obtained data, a model for predicting the yield of winter wheat in arid conditions of the Southern Steppe of Ukraine with a high level of significance (0.00009) was developed using power regression method.
... The Cheddock scale criteria[21] ...
Article
Full-text available
The purpose of this study is to observe the evaluation of all the factors that influence sustainable development, by doing this, the author collected all the logically affecting indicators of 2000–2018 and divided them into 4 groups by affiliation which are Economic and Political, Energy and Environmental, Innovation and Entrepreneurship, Intellect and Social Capital. This paper tries to perform the correlation coefficients matrix analysis to show, how the innovative indicators on sustainable development groups interact with each other, and open by using statistical methods to new views to further studies, in addition, to make the sustainable development activities of the Ukraine's energy enterprise sector more efficient and to pioneer further initiatives. The significance of the data was realized by using the normalization method, followed by using the Statistica mathematical program, and correlation coefficients were analyzed. At the last step, data were eliminated by applying the Cheddock scale. The data on the matrices that we built shows their noticeable significance and they are presented in this last stage of the study. According to the results of the study, the relationship between the data in each group has a high standing, and an innovative study has emerged with a statistical perspective. The resulting outcome demonstrates the connection of various 121 data and diversity between groups. The contribution of this study is that the results will be developed and reveal an integrated sustainable development mechanism and economic perspective with the final stage of the author’s prospected research. This article, as a part of the author's research, plans and provides an alternative viewpoint for energy venture companies within the framework of sustainable development pillars in UNDP.
... Статистичним аналізом було підтверджено помітний вплив кількості діючих речовин на вказаний показник (r= -0,63) [20]. Це співпадає з інформацією Windham & Windham [21], які стверджують, що системні фунгіциди, дія яких пов'язана із інгібуванням біосинтезу стеролів, проявляють рістрегулюючі властивості, що призводить до скорочених міжвузль та уповільнення росту. ...
Article
Full-text available
Sowing quality of winter wheat seeds depending on the component composition of protectants The influence of multicomponent protectants on the sowing quality of winter wheat seeds was studied. It was found that the presowing treatment reduces laboratory germination of seeds. With the statistical analysis the significant negative correlation effect (r = -0.63) of the amount of active ingredients in the protectant composition on the length of the seedling was found. The radicle length (r=-0,17) was weakly dependent on the component composition of protectants. Kantaris had the greatest depressant effect on seed sowing qualities. Keywords: soft winter wheat, multicomponent protectants, presowing treatment, germination, seedlings, radicles.
... Due to a limited training dataset, an ensemble learning strategy, namely Bootstrap aggregation, was applied in order to discriminate among different types of biological contamination and surfaces. Wavelet feature extraction and selection was also used prior to application of the analysis algorithm, as previously reported [20][21][22]. ...
... The LIF method with a dual wavelength sensing and multispectral pattern recognition enables BC-sense LIDAR a non-contact detection based on the primary component analysis of intrinsic fluorescence spectra of biological models [4]. According to current results, spectral shapes allowed distinguishing between groups of biological agents and background (e.g., culture medium, solvents and surface material) while minimizing false-positive results [22]. A panel of microbial agents on a range of solid surfaces were correctly classified without sample collection or analytical procedure and with a turnaround time <3 seconds for each identification test. ...
Article
Full-text available
A real-time detection and monitoring (RTDM) of microbial contamination on solid surfaces is mandatory in a range of security, safety and bio-medical applications where surfaces are exposed to accidental, natural or intentional microbial contamination. This work presents a new device, the BC-Sense, which allows a rapid and user-friendly RTDM of microbial contamination on various surfaces while assessing the decontamination kinetics and degree of cleanliness. The BC-Sense LIDAR (Light Detection and Ranging) device uses the Laser-Induced Fluorescence (LIF) method based on dual wavelength sensing with multispectral pattern recognition system to rapidly detect microbial contamination on a solid surface. Microbial simulants (bacteria, bacterial spores, fungal conidia and virus) were spread at varying concentrations on a panel of solid surfaces which were assessed by BC-Sense. The spectra of dead and living E. coli showed differences at various sensing wavelengths. Random samples (n=200) tested against a training data set (n=800) were optimally discriminated for contamination versus background with a threshold of predicted response (PR) >0.55 and <0.4, respectively. Decontamination kinetics on copper surface showed a complete disappearance of fluorescence in 1 min with MS2 versus >10 min with spores and E. coli.
... The expert software of the OWL™ utilized enhanced spectral pattern recognition technique based on the feature extraction with the wavelet transform for the structural decomposition of HLIF spectrum [8]. The feature extraction procedure serves to remove the irrelevant spectral information and provide dimensional reduction of the hypespectral data. ...
Article
Full-text available
The operational monitoring of the risk areas of marine environment requires cost-effective solutions. One of the options is the use of sensor networks based on fixed installations and moving platforms (coastal boats, supply-, cargo-, and passenger vessels). Such network allows to gather environmental data in time and space with direct links to operational activities in the controlled area for further environmental risk assessment. Among many remote sensing techniques the LiDAR (Light Detection And Ranging) based on Light Induced Fluorescence (LIF) is the tool of direct assessment of water quality variations caused by chemical pollution, colored dissolved organic matter, and phytoplankton composition. The Hyperspectral LIF (HLIF) LiDAR acquires comprehensive LIF spectra and analyses them by spectral pattern recognition technique to detect and classify the substances in water remotely. Combined use of HLIF LiDARs with Real-Time Data Management System (RTDMS) provides the economically effective solution for the regular monitoring in the controlled area. OCEAN VISUALS in cooperation with LDI INNOVATION has developed Oil in Water Locator (OWL™) with RTDMS (OWL MAP™) based on HLIF LiDAR technique. This is a novel technical solution for monitoring of marine environment providing continuous unattended operations. OWL™ has been extensively tested on board of various vessels in the North Sea, Norwegian Sea, Barents Sea, Baltic Sea and Caribbean Sea. This paper describes the technology features, the results of its operational use in 2014-2017, and outlook for the technology development. Keywords: Oil spill, fluorescence, lidar.
... The HLIF (Hyperspectral Laser Induced Fluorescence) LiDAR installed at a stationary or moving platform can serve as a basic sensor component of such monitoring network. It combines high sensitivity (part per millionppm level) and selectivity of oil-in-water detection with the analysis of comprehensive LIF spectra being based on spectral feature extraction (Sobolev and Babichenko 2013a) and spectral pattern recognition (Poryvkina et al. 2011) techniques. These characteristics make possible the use of this technique in different coastal and oceanic systems, and under variable environmental conditions (e.g. ...
Article
Full-text available
The accidental discharge or accumulated pollution in seawater constitutes high risk of damage to the marine environment. Early detection of deviations in water quality allows taking preventive measures in order to minimize harmful influence of pollution or to implement adequate response actions before the pollution becomes a major spill. This requires regular monitoring of risk areas with effective, reliable, and economically sustainable solutions. The Hyperspectral Laser Induced Fluorescence (HLIF) LiDAR (light detection and ranging) combines highly sensitive and selective oil-in-water detection with characterization capabilities based on feature extraction and pattern recognition in HLIF spectra. Therefore, this technique is equally effective for oil detection in open and coastal waters. Operated in unattended mode as a payload of marine vehicles, it delivers the real-time analytical capabilities directly on site. This article describes the development of HLIF LiDAR and its application on board of operational vessels in the Norwegian Sea, Barents Sea, and the Baltic Sea during the period 2014–2015.