Boundary Uncertainty Research Articles

Managing Boundary Uncertainty in Diagnosing the Patients of Rural Area Using Fuzzy and Rough Set.

People of rural India often suffer from acute health conditions like diarrhea, flu, lung congestion, and anemia, but they are not receiving treatment even at primary level due to scarcity of doctors and health infrastructure in remote villages. Health workers are involved in diagnosing the patients based on the symptoms and physiological signs. However, due to inadequate domain knowledge, lack of expertise, and error in measuring the health data, uncertainty creeps in the decision space, resulting many false cases in predicting the diseases. The paper proposes an uncertainty management technique using fuzzy and rough set theory to diagnose the patients with minimum false-positive and false-negative cases. We use fuzzy variables with proper semantic to represent the vagueness of input data, appearing due to measurement error. We derive initial degree of belonging of each patient in two different disease class labels (YES/NO) using the fuzzified input data. Next, we apply rough set theory to manage uncertainty in diagnosing the diseases by learning approximations of the decision boundary between the two class labels. The optimum lower and upper approximation membership functions for each disease class label have been obtained using Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Finally, using the proposed disease_similarity_factor, new patients are diagnosed precisely with 98% accuracy and minimum false cases.

Abstract 14436: Effects of Uncertainty in Border Delineation on GLS BL versus LVEF

Background: There are many clinical scenarios in which early detection of small changes in cardiac function can be critical in determining a patient’s treatment. Two of the most common imaging-based metrics in the clinic are LVEF and border-length-based GLS (GLS BL ). The aim of this project was to analyze the inherent biases and uncertainties in GLS BL relative to LVEF with respect to uncertainties in delineation of the endocardial border. Methods: I n silico 3D human LV endocardial surface models were created at end-diastole and end-systole based on MRIs of a healthy human volunteer. Twelve virtual long-axis imaging slices (in a radially-prescribed) were projected through the surface models to generate noise-free endocardial contour points. To simulate border delineation uncertainty, the set of endocardial contour points was subjected to inward and outward shifts of 1-5 mm, incrementally (Fig 1A). The expected uncertainty when using EC is around 3 mm. The % error vs. offsets were calculated for bi-plane GLS BL , bi-plane area-length LVEF AL , and bi-plane method-of-disks LVEF MOD , with 3D conformal surface-fit LVEF SURF as ground-truth. Results: The GLS BL %error vs. offset was linear (R 2 = 1.000) with a slope of 4.92 (Fig. 1B). Each of the LVEF measures were quadratic with offset (R 2 = 1.000), with slopes: LVEF AL : 6.68; LVEF MOD : 6.71; and LVEF SURF : 10.26. Conclusions: GLS BL is less sensitive to boundary delineation uncertainty than LVEF, due to the linear vs. quadratic dependence on offset. LVEF AL and LVEF MOD consistently underestimate true LVEF, making them less sensitive to boundary uncertainty. However, if border uncertainty is negligible, then LVEF is more sensitive than GLS BL to small changes in LV volume, and LVEF SURF is more sensitive than either LVEF AL and LVEF MOD .

RAPID UNCERTAINTY QUANTIFICATION OF THE STABILITY ANALYSIS USING A PROBABILISTIC ESTIMATION OF THE PROCESS FORCE PARAMETERS

In the course of the digitization of modern production systems, a reliable parameterization of the digital twin of machining processes is essential. For example, the digital representation of milling operations enables the process parameter selection without time-consuming and expensive test series by using stability lobe diagrams (SLD). However, the parameterization of the underlying process force model with very few cutting force experiments can prevent a reliable process design, as errors in the parameterization process are propagated to the stability analysis. Therefore, a novel two-step methodology is proposed to provide probabilistic credible intervals for conventional stability lobe diagrams: First, the unknown parameters of the process force model are estimated using a Bayesian regression method. Secondly, the estimated probability distributions of the process force parameters are used to quantify the uncertainty of the stability boundary using a mechanistic process force model. The proposed methodology is particularly characterized by its low computational cost, since time-consuming and computationally expensive Monte Carlo procedures are avoided. Instead, the methodology relies on the analytical derivation of the model parameters’ posterior probability distribution and on polynomial chaos expansion (PCE) algorithms to quantify the uncertainty in the final stability analysis.

A geostatistical implicit modeling framework for uncertainty quantification of 3D geo-domain boundaries: Application to lithological domains from a porphyry copper deposit

The spatial modeling of geo-domains has become ubiquitous in many geoscientific fields. However, geo-domains’ spatial modeling poses real challenges, including the uncertainty assessment of geo-domain boundaries. Geo-domain models are subject to uncertainties due mainly to the inherent lack of knowledge in areas with little or no data. Because they are often used for impactful decision-making, they must accurately estimate the geo-domain boundaries’ uncertainty. This paper presents a geostatistical implicit modeling method to assess the uncertainty of 3D geo-domain boundaries. The basic concept of the method is to represent the underlying implicit function associated with each geo-domain as a sum of a random implicit trend function and a residual random function. The conditional simulation of geo-domains is performed through a step-by-step approach. First, implicit trend function realizations and optimal covariance parameters associated with the residual random function are generated through the probability perturbation method. Then, residual function realizations are generated through classical geostatistical unconditional simulation methods and added to implicit trend function realizations to obtain unconditional implicit function realizations. Next, the conditioning of unconditional implicit function realizations to hard data is performed via principal component analysis and randomized quadratic programming. Finally, conditional implicit function simulations are transformed to conditional geo-domain simulations by applying a truncation rule. The proposed method is constructed to honor hard data and stated rules of how geo-domains interact spatially. It is applied to a lithological dataset from a porphyry copper deposit. A comparison with the classical sequential indicator simulation (SIS) method is carried out. The results indicate that the proposed approach can provide a more reliable and realistic uncertainty assessment of 3D geo-domain boundaries than the traditional sequential indicator simulation (SIS) approach.

An enhanced direct sampling (DS) approach to model the geological domain with locally varying proportions: Application to Golgohar iron ore mine, Iran

A critical challenge in evaluating mineral resources is to model the uncertainty of geological boundaries and to classify the block model into domains of similar rock or ore types. The aim of this study is to deal with the problem of simulating geological boundaries in the Golgohar iron deposit based on two datasets extracted from exploration boreholes and blast holes. In the first step, contact analysis tools are used to identify the nature of geological boundaries. Next, a modified version of the direct sampling algorithm is proposed to simulate geological domains based on a training image and local probabilities derived from a vertical proportion matrix. The purpose of using the modified direct sampling is to control the local proportion of each geological domain in the distance-based multiple-point geostatistical approaches. A set of 100 conditional realizations of two ore types are generated. The reconciliation between the realizations produced with the proposed algorithm and actual data values is provided by checking the reproduction of single-, two- and multiple-point statistical parameters and the connectivity of geological boundaries. The proposed method proves to be a useful variant of the direct sampling algorithm for simulating the spatial layout of geological domains when there is a strong difference in the domain proportions between the hard data and the TI.

New method for the reconstruction of sedimentary systems including lithofacies, environments, and flow paths: A case study of the Xisha Trough Basin, South China Sea

Existing seismic facies analysis schemes, including traditional seismic facies analysis, seismic slice analysis, and automatic classification of seismic facies, can reconstruct the sedimentary systems of basins; however, they are not ideal for basins with few boreholes and many rock types. Herein, we propose a new method for reconstructing sedimentary systems using seismic data, considering the Xisha Trough Basin in the Northern South China Sea as the research object to demonstrate the principles, methods, and technologies of this novel scheme. Based on the six commonly used seismic parameters, three additional seismic parameters (smoothness, neatness, and special waveform) and one geological marker (provenance) were added to improve the recognition of lithofacies. The scanned seismic interpretation technique was used to describe the distribution of sandstone and other skeleton lithofacies; sedimentary environments were determined according to the coastline, shelf break line, and slope toe line; flow path systems were determined according to the paleotopography, incised valleys, and sandstone distribution; and the sedimentary systems of the trough basin were reconstructed through the superposition of skeleton lithofacies, depositional environments, and flow paths. Results show that the sedimentary system reconstructed using the new method contains three elements, namely, lithofacies, the environment, and flow path, which fully reflect the concept of the sedimentary system defined by Fisher and McGowen (1967) ; this overcomes the inherent defects of previous schemes, such as poor lithofacies recognition and the uncertainty of types and boundaries of sedimentary bodies. This method enabled identifying subdivided seismic lithofacies directly from seismic profiles and enhanced the ability to obtain sedimentary information from seismic data. The proposed reconstruction method can be used in areas without boreholes or with many rock types and is significant for marine geology research and oil and gas exploration conducted in the deep sea.

Chance-constrained path planning in narrow spaces for a dubins vehicle

The problem of optimal path planning through narrow spaces in an unstructured environment is considered. The optimal path planning problem for a Dubins agent is formulated as a chance-constrained optimal control problem (CCOCP), wherein the uncertainty in obstacle boundaries is modelled using standard probability distributions. The chance constraints are transformed to deterministic equivalents using the inverse cumulative distribution function and subsequently incorporated into a deterministic optimal control problem. Due to multiple convex sub-regions introduced by the obstacles, the initial guess provided to optimal control solver is crucial for computation time and optimality of the solution. A constrained Delaunay triangulation mesh based approach is developed that ensures the initial guess to lie in the optimal sub-convex region. Finally, off-the-shelf software is used to transcribe the optimal control problem to a nonlinear program (NLP) using Gaussian quadrature orthogonal collocation and solved to obtain an optimal path that conforms to system dynamics. By varying the upper bound on probability of obstacle collision, a family of solutions is generated, parameterized by the risk associated with each solution. This approach enables discovery of special “keyhole trajectories” that can provide significant cost savings in a tightly-spaced obstacle field. Merits of this approach are illustrated by comparing it with the traditional bounded uncertainty approach.

Multivariate split moving windows and magnetic susceptibility for locating soil boundaries of São Paulo, Brazil

Multivariate split moving window (MSMW) is a tool to automate soil mapping and to assess uncertainty in soil boundaries. In this paper, we propose a new approach to locate soil boundaries. We investigated the potential of (i) MSMW associated with Mahalanobis D2 and (ii) split moving window (SMW) associated with magnetic susceptibility (MS) as tools for validation of soil boundaries. A transect was lined in Guatapará city (Sao Paulo state, Brazil), and 172 soil samples were collected from 86 locations, at a depth of 0–25 and 25–50 cm, for physical and chemical analyses. Additionally, categorical properties – geology, land use, and altitude – were assessed at the same sampling points. All the data were organized into four groups of properties (G1, G2, G3, and G4) and analyzed by principal component analysis, MSMW analysis – to delineate map units using D2 –, and SMW analysis using MS. MSMW and SMW were compared regarding their potential to locate soil boundaries along the transect. The MS peaks in SMW presented a correlation with peaks of t-statistics and D2 in MSMW: (r = 0.56; p < 0.01 – r = 0.69; p < 0.01) and (r = 0.55; p < 0.01 – r = 0.64; p < 0.01) for both depth intervals (0–25 cm and 25–50 cm), respectively. MSMW was more sensitive than SMW in the detection of soil boundaries in areas with lower clay content. Compared to conventional soil surveys, MSMW considerably improved the prediction of boundaries in mapping units. We concluded that the proposed method is a promising strategy for soil surveyors and can be used to assist pedological cartography.

Integrating Structural Symmetry and Local Homoplasy Information in Intuitionistic Fuzzy Clustering for Infrared Pedestrian Segmentation

Interferential background, boundary uncertainty, and noises are usually involved in infrared pedestrian imaging, which erect barrier for accurate segmentation. To counter the conundrum rising in these cases, we present a novel intuitionistic fuzzy clustering-based segmentation method, which integrates structural symmetry and local homoplasy information, for precise infrared pedestrian segmentation. Enlightened by the multiapplication and favorable performance of fuzzy clustering methods, intuitionistic fuzzy c-means (IFCM) is applied as the backbone of our segmentation method. The contributions of the proposed method mainly include two parts. First, motivated by potential target characteristics and tendency for clearer contour description, structural symmetry information is utilized, which is an intrinsic shape feature of objects and would be significant especially when the texture and details of the target are lost in infrared images. Further, a map that represents the probability of pixels belonging to the target is constructed in the form of ellipse symmetry region. Combined with the probability map, symmetry information is utilized to establish a novel dissimilarity function in fuzzy clustering. Second, local homoplasy information which is designed based on region similarity is introduced to suppress the intensity inhomogeneity and noises to further improve the performance of the proposed method. Finally, a dataset containing 500 infrared pedestrian images paired with corresponding pixel-wise annotation is constructed to verify segmentation effectiveness. The proposed SR-IFCM is compared with 12 state-of-the-art segmentation methods. The experimental results indicate that the proposed method outperforms the comparison methods and works better for infrared pedestrian segmentation.

Spatial Gaussian processes improve multi‐species occupancy models when range boundaries are uncertain and nonoverlapping

Abstract Species distribution models enable practitioners to analyze large datasets of encounter records and make predictions about species occurrence at unsurveyed locations. In omnibus surveys that record data on multiple species simultaneously, species ranges are often nonoverlapping and misaligned with the administrative unit defining the spatial domain of interest (e.g., a state or province). Consequently, some species display differentially restricted extents within a study area. Assuming hard boundaries based on expert opinion or published range maps to restrict species occurrence predictions implies a false sense of certainty in model‐based inferences. We propose a multi‐species occupancy model with a spatial Gaussian process on site‐specific effects for each species as a model‐based solution. Specifying informative Bayesian hyperpriors on the spatial hyperparameters encapsulates broad‐scale correlation among site occupancy probabilities for each species. We fit this model to acoustic detection/nondetection data collected with autonomous recording units during summer of 2016–2019 throughout Oregon and Washington, USA, on 15 bat species. We found vast improvements in spatial predictions of spotted bat (Euderma maculatum), canyon bat (Parastrellus hesperus), and Brazilian free‐tailed bat (Tadarida brasiliensis) when the available environmental predictors were insufficient for characterizing their restricted ranges within the region. In contrast, widespread species (Lasionycteris noctivagans, Myotis californicus, Myotis evotis, Myotis volans) were appropriately modeled using only environmental predictors, such as percentage forest cover and cliff and canyon cover. Utilizing spatial Gaussian processes within a community or multi‐species model incorporates uncertainty in range boundaries and allows for simultaneous predictions for the entire faunal assemblage even if species have nonoverlapping or restricted ranges within a spatial domain of interest. Such modeling improvements are essential if species distribution models are to accurately inform monitoring, species recovery plans, and other conservation efforts.

An Improved Boundary Uncertainty-Based Estimation for Classifier Evaluation

This paper proposes a new boundary uncertainty-based estimation method that has significantly higher accuracy, scalability, and applicability than our previously proposed boundary uncertainty estimation method. In our previous work, we introduced a new classifier evaluation metric that we termed “boundary uncertainty.” The name “boundary uncertainty” comes from evaluating the classifier based solely on measuring the equality between class posterior probabilities along the classifier boundary; satisfaction of such equality can be described as “uncertainty” along the classifier boundary. We also introduced a method to estimate this new evaluation metric. By focusing solely on the classifier boundary to evaluate its uncertainty, boundary uncertainty defines an easier estimation target that can be accurately estimated based directly on a finite training set without using a validation set. Regardless of the dataset, boundary uncertainty is defined between 0 and 1, where 1 indicates whether probability estimation for the Bayes error is achieved. We call our previous boundary uncertainty estimation method “Proposal 1” in order to contrast it with the new method introduced in this paper, which we call “Proposal 2.” Using Proposal 1, we performed successful classifier evaluation on real-world data and supported it with theoretical analysis. However, Proposal 1 suffered from accuracy, scalability, and applicability limitations owing to the difficulty of finding the location of a classifier boundary in a multidimensional sample space. The novelty of Proposal 2 is that it locally reformalizes boundary uncertainty in a single dimension that focuses on the classifier boundary. This convenient reduction with a focus toward the classifier boundary provides the new method’s significant improvements. In classifier evaluation experiments on Support Vector Machines (SVM) and MultiLayer Perceptron (MLP), we demonstrate that Proposal 2 offers a competitive classifier evaluation accuracy compared to a benchmark Cross Validation (CV) method as well as much higher scalability than both CV and Proposal 1.

Hydrogeological Uncertainty Estimation With the Analytic Element Method

AbstractUncertainty estimation plays an important part in practical hydrogeology. With most of the subsurface unobservable, attempts at system characterization will invariably be incomplete. Uncertainty estimation, then, must quantify the influence of unknown parameters, forcings, and structural deficiencies. In this endeavor, numerical modeling frameworks can resolve a high degree of subsurface complexity and its associated uncertainty. Where boundary uncertainty is concerned, however, numerical frameworks can be restrictive. The interdependence of grid discretization and its enclosing boundaries render exploration of uncertainties in their extent or nature challenging. The analytic element method (AEM) may be an interesting complement, as it is computationally efficient, economic with its parameter count, and does not require enclosure through finite boundaries. These properties make AEM well suited for uncertainty estimation, particularly in data‐scarce settings or exploratory studies. In this study, we explore the use of AEM for flow field uncertainty estimation, with a particular focus on boundary uncertainty. To induce diverse, uncertain regional flow more easily, we propose a new element based on a Möbius transformation. We include this element in a simple Python‐based AEM toolbox and benchmark it against MODFLOW. Coupling AEM with a Markov Chain Monte Carlo routine using adaptive proposals, we explore its use in a synthetic case study. We find that AEM permits efficient uncertainty estimation for groundwater flow fields, which may form a basis for stochastic Lagrangian transport modeling or can support advanced model design by informing the placement of numerical model boundaries.

The Observational Uncertainty of Coronal Hole Boundaries in Automated Detection Schemes

Abstract Coronal holes are the observational manifestation of the solar magnetic field open to the heliosphere and are of pivotal importance for our understanding of the origin and acceleration of the solar wind. Observations from space missions such as the Solar Dynamics Observatory now allow us to study coronal holes in unprecedented detail. Instrumental effects and other factors, however, pose a challenge to automatically detect coronal holes in solar imagery. The science community addresses these challenges with different detection schemes. Until now, little attention has been paid to assessing the disagreement between these schemes. In this COSPAR ISWAT initiative, we present a comparison of nine automated detection schemes widely applied in solar and space science. We study, specifically, a prevailing coronal hole observed by the Atmospheric Imaging Assembly instrument on 2018 May 30. Our results indicate that the choice of detection scheme has a significant effect on the location of the coronal hole boundary. Physical properties in coronal holes such as the area, mean intensity, and mean magnetic field strength vary by a factor of up to 4.5 between the maximum and minimum values. We conclude that our findings are relevant for coronal hole research from the past decade, and are therefore of interest to the solar and space research community.

Research into Power Transformer Health Assessment Technology Based on Uncertainty of Information and Deep Architecture Design

The uncertainty of the evaluation information is likely to affect the accuracy of the evaluation, when conducting a health evaluation of a power transformer. A multilevel health assessment method for power transformers is proposed in view of the three aspects of indicator criterion uncertainty, weight uncertainty, and fusion uncertainty. Firstly, indicator selection is conducted through the transformer guidelines and engineering experience to establish a multilevel model of transformers that can reflect the defect type and defect location. Then, a Gaussian cloud model is used to solve the uncertainty of the indicator criterion boundary. Based on association rules, AHP, and variable weights, the processed weights are calculated from the update module to obtain comprehensive weights, which overcomes the uncertainty of the weights. Improved DSmT theory is used for multiple evidence fusion to solve the high conflict and uncertainty problems in the fusion process. Finally, through actual case analysis, the defect type, defect location, and overall state of the transformer of the device are obtained. By comparing with many defect cases in a case-study library, the evaluation accuracy rate is found to reach 96.21%, which verifies the practicability and efficiency of the method.

Reducing Uncertainty in Mineralization Boundary by Optimally Locating Additional Drill Holes Through Particle Swarm Optimization

Reduction in uncertainty at the boundaries of ore deposits plays a critical role in mining projects. Such reductions can be made by choosing an appropriate objective function and using a suitable method for its optimization. In the literature, only a combined variance-based objective function can be found for modeling the problem of optimally locating additional drill holes to reduce uncertainty at the boundaries of mineralization. In this study, new objective functions based on interpolation variance (IV) and information entropy (IE) were developed and optimized through particle swarm optimization. The results of the proposed optimization methodology were compared with those of the combined variance-based objective function. Most of the proposed drill holes of the IV-based, IE-based and combined variance-based optimization methods are similar in many parts of the study area. However, IV-based and IE-based methods gave better results than the combined variance-based method. The proposed drill holes based on the IE-based objective function are better scattered over the area. This issue can be regarded as an advantage for the IE criterion both economically and technically.

Choosing between Gaussian and MPS simulation: the role of data information content\u2014a case study using uncertain interpretation data points

Integration of geophysical data with information such as from boreholes and expert domain knowledge is often performed as cognitive or explicit geological modeling when generating deterministic geological models of the subsurface. However, such cognitive geological models lack the ability to express the uncertainty of layer boundaries. To remedy the shortcomings of this strategy we propose a novel stochastic methodology combining the efforts of probabilistic data integration and cognitive modeling. We treat geological interpretation points from the cognitive model as uncertain “soft” data. These data are then combined with analogous geology in a probabilistic model. We test two ways of combining and sampling from such a probabilistic model. Firstly, a high-entropy setup based on Gaussian distributions simulation. Secondly, lower entropy (and conceivable more realistic) geological solutions are obtained from multiple-point geostatistics (MPS). We apply both ways of solving the problem at a study site near Horsens, Denmark, where airborne transient electromagnetic measurements, seismic data, and borehole information are available and interpreted in cognitive modeling. We explain the complete framework for integrating the uncertain interpretations in geostatistical simulation. Results show that both the Gaussian simulation and multiple-point geostatistical approach allows satisfactory simulations of uncertain geological interpretations and are consistent with prior geological knowledge. Our results suggest that the number of uncertain data points and their information content play a pivotal role in selecting the most appropriate simulation method for the given framework. MPS simulations allow connectivity in scenarios with few data points due to the low entropy of the model. When the number of soft data increases, Gaussian simulation is less prone to produce simulation artifacts, faster to apply, and could be considered preferential to using MPS simulation.

Smart determination of borehole number and locations for stability analysis of multi-layered slopes using multiple point statistics and information entropy

Subsurface stratigraphy of multi-layered slopes is essential and crucial for slope stability analysis. It is usual practice for engineers to interpret stratigraphic boundaries separating different soil layers using both site investigation data and prior knowledge of local geology, but such practice might encounter significant challenge when the site data are very limited. In addition, uncertainty in stratigraphic boundaries has not been explicitly or quantitatively considered in planning of site investigation (e.g., determination of borehole number and locations). There lacks a quantitative and objective tool to determine the optimal locations and number of boreholes for slope stability analysis while accounting for stratigraphic uncertainty. In this study, a smart sampling strategy based on multiple point statistics and information entropy is proposed for delineation of slope subsurface stratigraphy and planning of geotechnical boreholes. It is a data-driven approach that enables an ensemble of prior knowledge within a training image using multiple point statistics. The proposed method not only provides evolution of the most probable interpolation from sparse measurements and the associated interpolation uncertainties, but also adaptively determines the optimal locations of boreholes. Effectiveness of the proposed method is illustrated and validated through both a simulation example and a real case. It is found that the data-driven framework can automatically identify locations of largest interpolation uncertainty within a multi-layered slope conditional on its outcrops, and that the associated stratigraphic uncertainty gradually reduces as borehole number increases. More importantly, the optimal number and locations of boreholes required for slope stability analysis are adaptively determined by the proposed method.

ДЕФЕКТЫ МАГМОФОРМАЦИОННОГО АНАЛИЗА

The formation analysis of igneous rocks is a great achievement of the Soviet science in terms of classifying natural communities of magmatic rocks for the purpose of forecasting and prospecting of associated deposits. But due to the complexity of the problem itself, the uncertainty of boundaries between formational types in the practice of geological works, there are numerous defects that were clearly observed during the regional and interregional correlation of magmatic complexes of the Altai-Sayan region and Yenisey Ridge. The reasons and consequences of these defects, ways of their correction are analyzed.

Uncertainty of Forested Wetland Maps Derived from Aerial Photography

Forested wetlands (FWs) are economically and environmentally important, so monitoring of change is done using remote sensing by several U.S. federal programs. To better understand classification and delineation uncertainties in FW maps, we assessed agreement between National Wetlands Inventory maps based on aerial photography and field determinations at over 16 000 Forest Inventory and Analysis plots. Analyses included evaluation of temporal differences and spatial uncertainty in plot locations and wetland boundaries. User's accuracy for the wetlands map was 90% for FW and 68% for nonforested wetlands. High levels of false negatives were observed, with less than 40% of field-identified wetland plots mapped as such. Epsilon band analysis indicated that if delineation of FW boundaries in the southeastern U.S. met the data quality standards (5 meters), then the area within uncertainty bounds accounts for 15% to 30% of estimated FW area.

Computer-Integrated Device for Acidity Measurement Monitoring in Greenhouse Conditions with Compensation of Destabilizing Factors

The purpose of the article is to improve procedures of computerized monitoring and control of technological processes of growing greenhouse crops by substantiating methods of improving the accuracy of computer-integrated devices for measuring irrigation solution acidity. The article solves the topical scientific and applied problem of determining the conversion characteristics of computerized acidity monitoring systems with integral and differential assessment of their metrological parameters. Theoretical and experimental studies were obtained based on structural-algorithmic synthesis methods for information-measuring systems; methods of mathematical planning of experiments; regression analysis of experimental data and the concept of uncertainty. The computerized acidity meter was implemented on the basis of an ion-selective pH electrode, Arduino microprocessor platform, and ThingSpeak cloud computing service. The relative total boundary uncertainty of acidity measurement is not more than ±1.1 %. Methods of compensating of the random component of uncertainty based on the median filtering algorithm and additional uncertainty from the destabilizing effect of temperature were introduced when implementing the measuring device. Promising areas of priority research to improve the efficiency of the developed computerized acidity meter were justified. The developed device can be used in the complex automation of greenhouse cultivation processes. The developed and implemented measuring tool can be used when planning agricultural operations in greenhouse conditions.