scholarly journals Using Soft Sensors as a Basis of an Innovative Architecture for Operation Planning and Quality Evaluation in Agricultural Sprayers

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1269
Author(s):  
Elmer A. G. Peñaloza ◽  
Vilma A. Oliveira ◽  
Paulo E. Cruvinel
Keyword(s):  
Large Scale ◽  
Principal Component ◽  
Soft Sensor ◽  
Operating Conditions ◽  
K Nearest Neighbor ◽  
Pesticide Application ◽  
Soft Sensors ◽  
Agricultural Pesticides ◽  
Pcr Method

One of the major problems facing humanity in the coming decades is the production of food on a large scale. The production of large quantities of food must be conducted in a sustainable and responsible manner for nature and humans. In this sense, the appropriate application of agricultural pesticides plays a fundamental role since pesticide application in a qualified manner reduces human and environmental risks as well as the costs of food production. Evaluation of the quality of application using sprayers is an important issue, and several quality descriptors related to the average diameter and distribution of droplets are used. This paper describes the construction of a data-driven soft sensor using the parametric principal component regression (PCR) method based on principal component analysis (PCA), which works in two configurations: with the input being the operating conditions of the agricultural boom sprayers and its outputs being the prediction of the quality descriptors of spraying, and vice versa. The soft sensor provides, in one configuration, estimates of the quality of pesticide application at a certain time and, in the other, estimates of the appropriate sprayer-operating conditions, which can be used for control and optimization of the processes in pesticide application. Full cone nozzles are used to illustrate a practical application as well as to validate the usefulness of the soft sensor designed with the PCR method. The selection of historical data, exploration, and filtering of data, and the structure and validation of the soft sensor are presented. For comparison purposes, the results with the well-known nonparametric k-Nearest Neighbor (k−NN) regression method are presented. The results of this research reveal the usefulness of soft sensors in the application of agricultural pesticides and as a knowledge base to assist in agricultural decision-making.

scholarly journals Conservation of Mineral Elements in Maize Grains by a Triple Bagging System and Biopesticide (Lippia multiflora Moldenke and Hyptis suaveolens Poit Leaves).

2020 ◽  
Vol 8 (3) ◽  
Author(s):  
Gnande Romaric Die ◽  
Kouamé Olivier Chatigre ◽  
Ibrahim Fofana ◽  
N’guessan Verdier Abouo ◽  
Godi Henri Marius Biego
Keyword(s):  
Large Scale ◽  
Principal Component ◽  
Mineral Elements ◽  
Scale Production ◽  
Lippia Multiflora ◽  
Large Scale Production ◽  
Hyptis Suaveolens ◽  
Storage Methods ◽  
Central Composite

Maize (Zea mays) is a staple food in the traditional diet of rural populations in Côte d'Ivoire. It is a source of many minerals. However, inefficient and sometimes harmful storage methods hamper its large-scale production in Côte d'Ivoire. It is in this context that a triple bagging system associated or not with biopesticides of plant origin (Lippia multiflora and Hyptis suaveolens leaves) was proposed in this study to evaluate its efficacy on the conservation of mineral quality of grains over an 18-month period following a 3-factor central composite design (CCD). The first CCD factor consisted of 6 observation periods: 0; 1; 4.5; 9.5; 14.5 and 18 months. The second factor, the type of treatment, included 1 control lot with a polypropylene bag (TB0SP) and 9 experimental lots including 1 lot in triple bagging without biopesticides (TB0P) and the remaining 8 lots containing variable proportions and/or combinations of biopesticides (TB1 to TB8). And finally, the third factor was the combination of the two biopesticides with % Lippia multiflora as a reference. The results indicate that the shelf life, ratio and combination of biopesticides significantly (P < 0.05) influence the mineral quality of grain maize. Principal component analysis revealed that the addition of at least 1.01% biopesticides (leaves of Lippia multiflora and Hyptis suaveolens) in triple bagging systems improves preservation efficiency and preserves the mineral quality of the grain over a period of 15 months as opposed to triple bagging without biopesticides where the mineral elements are preserved during the first 10 months of storage. However, this preservation of mineral quality is more pronounced in these storage systems with combinations of biopesticides (of which the proportion is greater than or equal to 3.99%) or with 2.5 % of individual biopesticides.

scholarly journals Literature Consistency of Bioinformatics Sequence Databases is Effective for Assessing Record Quality

2017 ◽  
Author(s):  
Mohamed Reda Bouadjenek ◽  
Karin Verspoor ◽  
Justin Zobel
Keyword(s):  
Data Quality ◽  
Quality Indicators ◽  
Large Scale ◽  
Genomic Data ◽  
Principal Component ◽  
Mutual Relationship ◽  
Query Quality ◽  
Automatic Methods ◽  
Sequence Databases

AbstractBioinformatics sequence databases such as Genbank or UniProt contain hundreds of millions of records of genomic data. These records are derived from direct submissions from individual laboratories, as well as from bulk submissions from large-scale sequencing centres; their diversity and scale means that they suffer from a range of data quality issues including errors, discrepancies, redundancies, ambiguities, incompleteness, and inconsistencies with the published literature. In this work, we seek to investigate and analyze the data quality of sequence databases from the perspective of a curator, who must detect anomalous and suspicious records.Specifically, we emphasize the detection of inconsistent records with respect to the literature. Focusing on GenBank, we propose a set of 24 quality indicators, which are based on treating a record as a query into the published literature, and then use query quality predictors. We then carry out an analysis that shows that the proposed quality indicators and the quality of the records have a mutual relationship, in which one depends on the other. We propose to represent record-literature consistency as a vector of these quality indicators. By reducing the dimensionality of this representation for visualization purposes using Principal Component Analysis, we show that records which have been reported as inconsistent with the literature fall roughly in the same area, and therefore share similar characteristics. By manually analyzing records not previously known to be erroneous that fall in the same area than records know to be inconsistent, we show that 1 record out of 4 is inconsistent with respect to the literature. This high density of inconsistent record opens the way towards the development of automatic methods for the detection of faulty records. We conclude that literature inconsistency is a meaningful strategy for identifying suspicious records.

Soft Sensor Modeling via Support Vector Regression with KICA-Based Feature Extraction

2011 ◽  
Vol 186 ◽  
pp. 560-564
Author(s):  
Yi Liu ◽  
Hong Ying Deng ◽  
Zeng Liang Gao ◽  
Ping Li
Keyword(s):  
Support Vector Regression ◽  
Principal Component ◽  
Component Analysis ◽  
Soft Sensor ◽  
Modeling Method ◽  
Operating Conditions ◽  
Support Vector ◽  
Kernel Independent Component Analysis ◽  
Input Variables ◽  
Sensor Modeling

A novel two-level integrated soft sensor modeling method using kernel independent component analysis (KICA) and support vector regression (SVR) is proposed for chemical processes. In the first level, the KICA approach is adopted to extract information of input variables in the high dimensional feature space. Based on this strategy, the correlation of input variables can be eliminated and thus the complexity is reduced. Then, the model is established using SVR in the second level. The KICA-SVR soft sensor modeling method is applied to estimate product compositions in the Tennessee Eastman process. The obtained results show that it can exhibit better performance, compared to the traditional ICA, principal component analysis (PCA) and kernel PCA based information extraction methods, under different operating conditions.

scholarly journals SOM-Based Approach for the Analysis and Classification of Synchronous Impulsive Noise of an In-Ship PLC System

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
G. Acciani ◽  
V. Amoruso ◽  
G. Fornarelli ◽  
A. Giaquinto
Keyword(s):  
Impulsive Noise ◽  
Principal Component ◽  
Operating Conditions ◽  
Critical Conditions ◽  
Power Line Communications ◽  
High Data ◽  
Wideband Signals ◽  
Communication Frameworks

The interest in wideband data transmission over power line communications has increased rapidly. This technology offers a convenient and inexpensive medium to transmit data, reducing the number of cables. This advantage is particularly appealing in many fields, like the railway, naval, and aeronautical ones. Nevertheless, several problems have to be faced to obtain a high data rate. In particular, the presence of noise makes the transmission difficult, degrading the quality of received signals and prohibiting the full application of these communication frameworks. In this paper the behaviour of an in-ship powerline communication system is analyzed in the presence of synchronous periodic impulsive noise. Such noise is modelled at source and its effects on the transmission of wideband signals are evaluated by means of a simulation circuit model. The obtained results allow to identify the characteristics of the channel and the critical conditions due to noise. Subsequently, an unsupervised technique based on principal component analysis and fuzzy c-mean classifier detects the presence and classifies the specific noises. Numerical results show that the proposed approach enables to achieve this target accurately under different operating conditions, proving to be an effective tool to enhance the performances of the considered technology.

scholarly journals Automated analysis of large-scale NMR data generates metabolomic signatures and links them to candidate metabolites

2019 ◽  
Author(s):  
Bita Khalili ◽  
Mattia Tomasoni ◽  
Mirjam Mattei ◽  
Roger Mallol Parera ◽  
Reyhan Sonmez ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Human Urine ◽  
Nmr Spectra ◽  
Large Scale ◽  
Principal Component ◽  
Automated Analysis ◽  
Urine Samples ◽  
H Nmr ◽  
Nmr Data

AbstractIdentification of metabolites in large-scale 1H NMR data from human biofluids remains challenging due to the complexity of the spectra and their sensitivity to pH and ionic concentrations. In this work, we test the capacity of three analysis tools to extract metabolite signatures from 968 NMR profiles of human urine samples. Specifically, we studied sets of co-varying features derived from Principal Component Analysis (PCA), the Iterative Signature Algorithm (ISA) and Averaged Correlation Profiles (ACP), a new method we devised inspired by the STOCSY approach. We used our previously developed metabomatching method to match the sets generated by these algorithms to NMR spectra of individual metabolites available in public databases. Based on the number and quality of the matches we concluded that both ISA and ACP can robustly identify about a dozen metabolites, half of which were shared, while PCA did not produce any signatures with robust matches.

Development of Fault Specific Soft Sensors With Application to Gas Turbine Diagnosis

2012 ◽  
Author(s):  
Dorin Scheianu
Keyword(s):  
Gas Turbine ◽  
Dimensional Space ◽  
Analysis Data ◽  
Soft Sensor ◽  
Operating Conditions ◽  
Soft Sensors ◽  
Multivariate Statistical ◽  
Current Observation ◽  
Observation Vector ◽  
Turbine Installation

The paper presents a method of diagnosis for a gas turbine installation monitored for a number of parameters by using tools specific to multivariate statistical analysis. Data is acquired periodically and organized as individual observation vectors. The objective is to detect the occurrence of a fault, to identify and discriminate the type of fault and eventually to find its cause. The author developed a new concept of soft sensor (similar to the sensor fusion in other works, see [1]) based on the intrinsic properties of the data historically acquired when the process was deemed to be in normal operating conditions, and on the current observation vector. This soft sensor was applied to an example of detecting the occurrence of a fault and characterizing it in the p-dimensional space of observation vectors associated with a proper metric.

scholarly journals Soft sensor based on Gaussian process regression and its application in erythromycin fermentation process

2016 ◽  
Vol 22 (2) ◽  
pp. 127-135 ◽  
Author(s):  
Congli Mei ◽  
Ming Yang ◽  
Dongxin Shu ◽  
Hui Jiang ◽  
Guohai Liu ◽  
...  
Keyword(s):  
Gaussian Process ◽  
High Performance ◽  
Fermentation Process ◽  
Low Cost ◽  
Gaussian Process Regression ◽  
Principal Component ◽  
Soft Sensor ◽  
Support Vector ◽  
Soft Sensors ◽  
Uncertainty Measurement

Erythromycin fermentation process is a typical microbial fermentation process. Soft sensors can be used to estimate biomass of Erythromycin fermentation process for their relative low cost, simple development, and ability to predict difficult-to-measure variables. However, traditional soft sensors, e.g. artificial neural network (ANN) soft sensors, support vector machine (SVM) soft sensors, etc., cannot represent the uncertainty (measurement precision) of outputs. That results in difficulties in practice. Gaussian process regression (GPR) provides a novel framework to solve regression problems. The output uncertainty of a GPR model follows Gaussian distribution, expressed in terms of mean and variance. The mean represents the predicted output. The variance can be viewed as the measure of confidence in the predicted output that distinguishes the GPR from NN and SVM soft sensor models. We proposed a systematic approach based on GPR and principal component analysis (PCA) to establish a soft sensor to estimate biomass of Erythromycin fermentation process. Simulations on industrial data from an Erythromycin fermentation process show the proposed GPR soft sensor has high performance of modeling the uncertainty of estimates.

scholarly journals IMAGE MOSAIC ALGORITHM BASED ON PCA-ORB FEATURE MATCHING

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 83-89
Author(s):  
J. T. Zhu ◽  
C. F. Gong ◽  
M. X. Zhao ◽  
L. Wang ◽  
Y. Luo
Keyword(s):  
Principal Component Analysis ◽  
Large Scale ◽  
Nearest Neighbor ◽  
Principal Component ◽  
Component Analysis ◽  
Feature Descriptor ◽  
Image Stitching ◽  
Feature Points ◽  
K Nearest Neighbor ◽  
Fusion Algorithm

Abstract. In the process of image stitching, the ORB (Oriented FAST and Rotated BRIEF) algorithm lacks the characteristics of scale invariance and high mismatch rate. A principal component invariant feature transform (PCA-ORB, Principal Component Analysis- Oriented) is proposed. FAST and Rotated BRIEF) image stitching method. Firstly, the ORB algorithm is used to optimize the feature points to obtain the feature points with uniform distribution. Secondly, the principal component analysis (PCA) method can reduce the dimension of the traditional ORB feature descriptor and reduce the complexity of the feature point descriptor data. Thirdly, KNN (K-Nearest Neighbor) is used, and the k-nearest neighbor algorithm performs roughly matching on the feature points after dimensionality reduction. Then the random matching consistency algorithm (RANSAC, Random Sample Consensus) is used to remove the mismatched points. Finally, the fading and fading fusion algorithm is used to fuse the images. In 8 sets of simulation experiments, the image stitching speed is improved relative to the PCA-SIFT algorithm. The experimental results show that the proposed algorithm improves the image stitching speed under the premise of ensuring the quality of stitching, and can play a role in fast, real-time and large-scale applications, which are conducive to image fusion.

Soft sensor design for dry substance content estimation in the sugar industry

2012 ◽  
pp. 645-653 ◽  
Author(s):  
Diego Garcia-Alvarez ◽  
Alejandro Merino ◽  
Ruben Martí ◽  
Maria Jesus Fuente
Keyword(s):  
Neural Networks ◽  
Sugar Industry ◽  
Principal Component ◽  
Real Data ◽  
Correlation Study ◽  
Soft Sensor ◽  
Soft Sensors ◽  
Indirect Measurements ◽  
The Third ◽  
Dry Substance

Four techniques are studied to design a soft sensor for dry substance content estimation (% DS) in the sugar industry. Dry substance content sensors are in general expensive and inaccurate, so it is interesting to study and develop soft sensors for this variable. Concretely, the dry substance content of the juice leaving the evaporation station has been estimated. For that purpose, four methods have been proposed. The first one is based on indirect measurements, using physicochemical properties. The second one uses neural networks where the inputs to the net are selected manually, based on a correlation study of the variables of the evaporation station. The third one uses neural networks whose inputs are the scores calculated by means of Principal Component Analysis (PCA). The last method uses an estimation based on Partial Least Squares (PLS) regression. This paper explains, compares and analyses the results obtained using real data collected from the plant.

scholarly journals Investigation of fungicidal activity of biocides by method of direct contact with test cultures

2018 ◽  
Vol 251 ◽  
pp. 01018 ◽  
Author(s):  
Valeriya Strokova ◽  
Viktoriya Nelyubova ◽  
Marina Rykunova
Keyword(s):  
Direct Contact ◽  
Large Scale ◽  
Fungicidal Activity ◽  
Cost Savings ◽  
Full Scale ◽  
Operating Conditions ◽  
Express Method ◽  
Test Cultures ◽  
Full Scale Testing

The article substantiates the possibility of using the method of direct contact with test cultures as an express method for assessing the quality of biocidal substances. This technique allows to test soluble biocidal preparations, while reducing the time of biocide selection, which eliminates the need for large-scale full-scale testing of structures under real operating conditions, providing cost savings.

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