Constrained discrete model predictive control of a greenhouse system temperature

In this paper, a constrained discete model predictive control (CDMPC) strategy for a greenhouse inside temperature is presented. To describe the dynamics of our system’s inside temperature, an experimental greenhouse prototype is engaged. For the mathematical modeling, a state space form which fits properly the acquired data of the greenhouse temperature dynamics is identified using the subspace system identification (N4sid) algorithm. The obtained model is used in order to develop the CDMPC starategy which role is to select the best control moves based on an optimization procedure under the constraints on the control notion. For efficient evaluation of the proposed control approach Matlab/Simulink and Yalmip optimization toolbox are used for algorithm and blocks implementation. The simulation results confirm the accuracy of the controller that garantees both the control and the reference tracking objectives

Hybrid Electronic Tongue based on Multisensor Data Fusion for Discrimination of Beers

This paper reports the use of a hybrid Electronic Tongue based on data fusion of two different sensor families, applied in the recognition of beer types. Six modifiedgraphite- epoxy voltammetric sensors plus 15 potentiometric sensors formed the sensor array. The different samples were analyzed using cyclic voltammetry and direct potentiometry without any sample pretreatment in both cases. The sensor array coupled with feature extraction and pattern recognition methods, namely Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), was trained to classify the data clusters related to different beer varieties. PCA was used to visualize the different categories of taste profiles and LDA with leave-one-out cross-validation approach permitted the qualitative classification. The aim of this work is to improve performance of existing electronic tongue systems by exploiting the new approach of data fusion of different sensor types

Detection of Adulteration in Argan Oil by Using an Electronic Nose and a Voltammetric Electronic Tongue

Adulteration detection of argan oil is one of the main aspects of its quality control. Following recent fraud scandals, it is mandatory to ensure product quality and customer protection. The aim of this study is to detect the percentages of adulteration of argan oil with sunflower oil by using the combination of a voltammetric e-tongue and an e-nose based on metal oxide semiconductor sensors and pattern recognition techniques. Data analysis is performed by three pattern recognition methods: principal component analysis (PCA), discriminant factor analysis (DFA), and support vector machines (SVMs). Excellent results were obtained in the differentiation between unadulterated and adulterated argan oil with sunflower one. To the best of our knowledge, this is the first attempt to demonstrate whether the combined e-nose and e-tongue technologies could be successfully applied to the detection of adulteration of argan oil

Understanding graphic narrative through the synthesis of comic and picturebooks

This study was undertaken to develop a better understanding of comics, picturebooks, and their relationship through progressive attempts to combine them in practice. The study was motivated by an interest in hybrid forms as a site where narrative techniques from different forms are put to alternative use in a new context. The research contributes to current scholarly discussion of graphic narrative from a practitioner’s perspective. Reflective practice offers unique potential as a method for critical study. Comparative analysis of changes over time throws light on each form’s typical mechanisms for graphic storytelling, and demonstrates their function in different contexts. Problems arising in practice are catalysts for a process of dynamic, analogical theory-formation and -testing, which often challenges or supplements existing knowledge, leading to a more nuanced understanding of the forms with which practice engages. Findings evolved, firstly, from the insight that conventions for graphic storytelling function differently depending on the mode of reading and the formal context. Secondly, the degree to which the practitioner is constrained by formal limitations was found to demand a disciplined distillation of content that deliberately creates space for different kinds of readerly engagement. The study concluded that, due to their adaptation towards solitary reading, comics exert greater control over their readers, whereas picturebooks tend to be more flexible in order to accommodate different modes of reading. The way readers engage with a work impacts on the function of conventions and techniques for graphic storytelling as much as a change in formal context. Moreover, the discipline of the picturebook form demands greater economy, which can create more space for reader participation. However, neither distinct modes of reading nor differing degrees of constraint constitute grounds for definitive distinction between comics and picturebooks: instead, they offer alternative frameworks for the critical consideration of graphic narratives

Conception and Development of a Portable Electronic Nose System for Classification of Raw Milk Using Principal Component Analysis Approach

The analysis of the aroma of milk is an especially complex problem due to the heterogeneous nature of milk. In the present study, a portable electronic nose has been fabricated and characterized using an oxide semiconductor gas sensor array. The portable electronic nose system, based on Taguchi Gas Sensors (TGS), consists of a microcontroller PIC16F877 as CPU, an LCD for displaying gas conductance and a LabVIEW© PC interface for data acquisition, etc. To check its separation capability a pattern recognition method namely Principal Component Analysis (PCA) has been performed. The PCA method permits a good classification between three types of raw milks from different dairy farms. On the other hand, the data coming from the response of the sensors have been elaborated by PCA and Support Vectors Machines (SVMs) in order to obtain a classification of the data clusters related to different milk ageing days and so track the dynamic evolution of milk rancidity. It was found that the portable electronic nose system together with a pattern recognition technique, PCA or SVMs is able to show a characteristic development of the milk quality, when it is stored at 4 °C, dependent on storage time. The last section draws the evaluation of the hygienic quality of raw milk, the following microbiological counts were determined: yeast and mould counts, total coliform count and total aerobic mesophilic flora count

Electronic Nose Based on Metal Oxide Semiconductor Sensors as an Alternative Technique for the Spoilage Classification of Red Meat

The aim of the present study was to develop an electronic nose for the quality control of red meat. Electronic nose and bacteriological measurements are performed to analyse samples of beef and sheep meat stored at 4°C for up to 15 days. Principal component analysis (PCA) and support vector machine (SVM) based classification techniques are used to investigate the performance of the electronic nose system in the spoilage classification of red meats. The bacteriological method was selected as the reference method to consistently train the electronic nose system. The SVM models built classified meat samples based on the total microbial population into “unspoiled†(microbial counts 6 log10 cfu/g). The preliminary results obtained by the bacteria total viable counts (TVC) show that the shelf-life of beef and sheep meats stored at 4 °C are 7 and 5 days, respectively. The electronic nose system coupled to SVM could discriminate between unspoiled/ spoiled beef or sheep meats with a success rate of 98.81 or 96.43 %, respectively. To investigate whether the results of the electronic nose correlated well with the results of the bacteriological analysis, partial least squares (PLS) calibration models were built and validated. Good correlation coefficients between the electronic nose signals and bacteriological data were obtained, a clear indication that the electronic nose system can become a simple and rapid technique for the quality control of red meats

Discrimination of Diabetes Mellitus Patients and Healthy Individuals Based on Volatile Organic Compounds (VOCs): Analysis of Exhaled Breath and Urine Samples by Using E-Nose and VE-Tongue

Studies suggest that breath and urine analysis can be viable non-invasive methods for diabetes management, with the potential for disease diagnosis. In the present work, we employed two sensing strategies. The first strategy involved analyzing volatile organic compounds (VOCs) in biological matrices, such as exhaled breath and urine samples collected from patients with diabetes mellitus (DM) and healthy controls (HC). The second strategy focused on discriminating between two types of DM, related to type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM), by using a data fusion method. For this purpose, an electronic nose (e-nose) based on five tin oxide (SnO2) gas sensors was employed to characterize the overall composition of the collected breath samples. Furthermore, a voltametric electronic tongue (VE-tongue), composed of five working electrodes, was dedicated to the analysis of urinary VOCs using cyclic voltammetry as a measurement technique. To evaluate the diagnostic performance of the electronic sensing systems, algorithm tools including principal component analysis (PCA), discriminant function analysis (DFA) and receiver operating characteristics (ROC) were utilized. The results showed that the e-nose and VE-tongue could discriminate between breath and urine samples from patients with DM and HC with a success rate of 99.44% and 99.16%, respectively. However, discrimination between T1DM and T2DM samples using these systems alone was not perfect. Therefore, a data fusion method was proposed as a goal to overcome this shortcoming. The fusing of data from the two instruments resulted in an enhanced success rate of classification (i.e., 93.75% for the recognition of T1DM and T2DM)

Full Paper Monitoring the Freshness of Moroccan Sardines with a Neural-Network Based Electronic Nose

Abstract: An electronic nose was developed and used as a rapid technique to classify the freshness of sardine samples according to the number of days spent under cold storage (4 ± 1°C, in air). The volatile compounds present in the headspace of weighted sardine samples were introduced into a sensor chamber and the response signals of the sensors were recorded as a function of time. Commercially available gas sensors based on metal oxide semiconductors were used and both static and dynamic features from the sensor conductance response were input to the pattern recognition engine. Data analysis was performed by three different pattern recognition methods such as probabilistic neural networks (PNN), fuzzy ARTMAP neural networks (FANN) and support vector machines (SVM). The objective of this study was to find, among these three pattern recognition methods, the most suitable one for accurately identifying the days of cold storage undergone by sardine samples. The results show that the electronic nose can monitor the freshness of sardine samples stored at 4°C, and that the best classification and prediction are obtained with SVM neural network. The SVM approach shows improved classificatio

Monitoring the Freshness of Moroccan Sardines with a Neural-Network Based Electronic Nose

An electronic nose was developed and used as a rapid technique to classify thefreshness of sardine samples according to the number of days spent under cold storage (4 ±1°C, in air). The volatile compounds present in the headspace of weighted sardine sampleswere introduced into a sensor chamber and the response signals of the sensors wererecorded as a function of time. Commercially available gas sensors based on metal oxidesemiconductors were used and both static and dynamic features from the sensorconductance response were input to the pattern recognition engine. Data analysis wasperformed by three different pattern recognition methods such as probabilistic neuralnetworks (PNN), fuzzy ARTMAP neural networks (FANN) and support vector machines(SVM). The objective of this study was to find, among these three pattern recognitionmethods, the most suitable one for accurately identifying the days of cold storage undergoneby sardine samples. The results show that the electronic nose can monitor the freshness ofsardine samples stored at 4°C, and that the best classification and prediction are obtainedwith SVM neural network. The SVM approach shows improved classificationperformances, reducing the amount of misclassified samples down to 3.75 %

Formaldehyde detection with chemical gas sensors based on WO3 nanowires decorated with metal nanoparticles under dark conditions and UV light irradiation

We report results of formaldehyde gas (CH2O) detection under dark conditions and UV light irradiation with pristine tungsten trioxide nanowires (WO3 NWs) and metal nanoparticles decorated WO3 NWs gas sensing layers. The resistive layers were deposited by one step aerosol assisted chemical vapor deposition (AACVD) on commercial alumina substrates with 10-pair interdigitated platinum electrodes. The elaborated gas sensors, based on pristine WO3 and on WO3 decorated with Au, Pt, Au/Pt, Ni and Fe nanoparticles, were investigated towards three concentrations of formaldehyde gas (5, 10 and 15 ppm) under dark conditions and under UV light irradiation at the wavelength of 394 nm. Two main effects were observed: firstly, under UV light irradiation the response time for CH2O desorption was significantly reduced with the exception of the nanomaterial with Fe NPs dopant; secondly, the gas induced baseline shift was reduced under UV light irradiation conditions. These results can be explained by the additional energy induced by the UV light, accelerating the adsorption-desorption processes. The results obtained confirmed that both the decoration of WO3 NWs with selected metal nano particles as well as sensors operation under UV light irradiation are a practical and affordable way to enhance gas sensing towards formaldehyde detection, although both strategies applied together did not introduce an amplified synergetic effect