An ANN Model to Estimate the Impact of Tea Process Parameters on Tea Quality

2015 ◽  
Vol 24 (09) ◽  
pp. 1550139
Author(s):  
Debashis Saikia ◽  
Diganta Kumar Sarma ◽  
P. K. Boruah ◽  
Utpal Sarma
Keyword(s):  
Process Condition ◽  
Correlation Study ◽  
Sensor Nodes ◽  
Quality Data ◽  
Inlet Temperature ◽  
Ann Model ◽  
Classification Rate ◽  
Tea Quality ◽  
Artificial Neural Network Ann ◽  
The Impact

Present study deals with the development of an artificial neural network (ANN)-based technique for tea quality quantification by monitoring fermentation and drying condition of the tea processing stages. An RS485 network-based instrumentation system has been developed and implemented for data collection for these two stages. Three calibrated sensor nodes are installed in the fermentation room due to its larger floor area to collect temperature and relative humidity (RH). Dryer inlet temperature is recorded using a calibrated thermocouple-based sensor node. From seven input parameters and target quality data obtained from tea taster, the ANN model has been developed to find the correlation between the process condition and the tea quality. From the correlation study, more than 90% classification rate is obtained from the model. The model is also validated with some independent data showing more than 60% correlation. Error in terms of root mean square error (RMSE) is about 0.17. This model will be helpful for improvement of tea quality.

scholarly journals Impact of biomass burning emission on total peroxy nitrates: fire plume identification during the BORTAS campaign

2016 ◽  
Vol 9 (11) ◽  
pp. 5591-5606 ◽  
Author(s):  
Eleonora Aruffo ◽  
Fabio Biancofiore ◽  
Piero Di Carlo ◽  
Marcella Busilacchio ◽  
Marco Verdecchia ◽  
...  
Keyword(s):  
Biomass Burning ◽  
Atmospheric Pressure ◽  
Thermal Dissociation ◽  
Ann Model ◽  
Fire Plume ◽  
Pressure Threshold ◽  
Peroxy Nitrates ◽  
Artificial Neural Network Ann ◽  
The Impact

Abstract. Total peroxy nitrate ( ∑ PN) concentrations have been measured using a thermal dissociation laser-induced fluorescence (TD-LIF) instrument during the BORTAS campaign, which focused on the impact of boreal biomass burning (BB) emissions on air quality in the Northern Hemisphere. The strong correlation observed between the  ∑ PN concentrations and those of carbon monoxide (CO), a well-known pyrogenic tracer, suggests the possible use of the  ∑ PN concentrations as marker of the BB plumes. Two methods for the identification of BB plumes have been applied: (1)  ∑ PN concentrations higher than 6 times the standard deviation above the background and (2)  ∑ PN concentrations higher than the 99th percentile of the  ∑ PNs measured during a background flight (B625); then we compared the percentage of BB plume selected using these methods with the percentage evaluated, applying the approaches usually used in literature. Moreover, adding the pressure threshold ( ∼  750 hPa) as ancillary parameter to  ∑ PNs, hydrogen cyanide (HCN) and CO, the BB plume identification is improved. A recurrent artificial neural network (ANN) model was adapted to simulate the concentrations of  ∑ PNs and HCN, including nitrogen oxide (NO), acetonitrile (CH3CN), CO, ozone (O3) and atmospheric pressure as input parameters, to verify the specific role of these input data to better identify BB plumes.

scholarly journals Antioxidant Activity of Selected Polyphenolics in Yeast Cells: The Case Study of Montenegrin Merlot Wine

Molecules ◽  
2018 ◽  
Vol 23 (8) ◽  
pp. 1971 ◽  
Author(s):  
Neda Đorđević ◽  
Nevena Todorović ◽  
Irena Novaković ◽  
Lato Pezo ◽  
Boris Pejin ◽  
...  
Keyword(s):  
Antioxidant Activity ◽  
Antioxidant Defense System ◽  
Yeast Cells ◽  
Total Phenolic ◽  
Ann Model ◽  
Phenolic Components ◽  
Stressed Cells ◽  
Pre Treatment ◽  
Artificial Neural Network Ann ◽  
The Impact

Screens of antioxidant activity (AA) of various natural products have been a focus of the research community worldwide. This work aimed to differentiate selected samples of Merlot wines originated from Montenegro, with regard to phenolic profile and antioxidant capacity studied by survival rate, total sulfhydryl groups and activities of glutathione peroxidase (GPx), glutathione reductase and catalase in H2O2–stressed Saccharomyces cerevisiae cells. In this study, DPPH assay was also performed. Higher total phenolic content leads to an enhanced AA under both conditions. The same trend was observed for catechin and gallic acid, the most abundant phenolics in the examined wine samples. Finally, the findings of an Artificial Neural Network (ANN) model were in a good agreement (r2 = 0.978) with the experimental data. All tested samples exhibited a protective effect in H2O2–stressed yeast cells. Pre-treatment with examined wines increased survival in H2O2–stressed cells and shifted antioxidative defense towards GPx–mediated defense. Finally, sensitivity analysis of obtained ANN model highlights the complexity of the impact that variations in the concentrations of specific phenolic components have on the antioxidant defense system.

scholarly journals Predicting Runoff Chloride Concentrations in Suburban Watersheds Using an Artificial Neural Network (ANN)

Hydrology ◽  
2020 ◽  
Vol 7 (4) ◽  
pp. 80
Author(s):  
Khurshid Jahan ◽  
Soni M. Pradhanang
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Sodium Chloride ◽  
Stormwater Runoff ◽  
Quality Data ◽  
Ann Model ◽  
Water Quality Data ◽  
Artificial Neural Network Ann ◽  
Chloride Concentrations

Road salts in stormwater runoff, from both urban and suburban areas, are of concern to many. Chloride-based deicers [i.e., sodium chloride (NaCl), magnesium chloride (MgCl2), and calcium chloride (CaCl2)], dissolve in runoff, travel downstream in the aqueous phase, percolate into soils, and leach into groundwater. In this study, data obtained from stormwater runoff events were used to predict chloride concentrations and seasonal impacts at different sites within a suburban watershed. Water quality data for 42 rainfall events (2016–2019) greater than 12.7 mm (0.5 inches) were used. An artificial neural network (ANN) model was developed, using measured rainfall volume, turbidity, total suspended solids (TSS), dissolved organic carbon (DOC), sodium, chloride, and total nitrate concentrations. Water quality data were trained using the Levenberg-Marquardt back-propagation algorithm. The model was then applied to six different sites. The new ANN model proved accurate in predicting values. This study illustrates that road salt and deicers are the prime cause of high chloride concentrations in runoff during winter and spring, threatening the aquatic environment.

scholarly journals Prediction Model for Random Variation in FinFET Induced by Line-Edge-Roughness (LER)

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 455
Author(s):  
Jinwoong Lee ◽  
Taeeon Park ◽  
Hongjoon Ahn ◽  
Jihwan Kwak ◽  
Taesup Moon ◽  
...  
Keyword(s):  
Prediction Model ◽  
Threshold Voltage ◽  
Drain Current ◽  
Ann Model ◽  
Line Edge Roughness ◽  
Threshold Voltages ◽  
Edge Roughness ◽  
Artificial Neural Network Ann ◽  
The Impact ◽  
Random Variation

As the physical size of MOSFET has been aggressively scaled-down, the impact of process-induced random variation (RV) should be considered as one of the device design considerations of MOSFET. In this work, an artificial neural network (ANN) model is developed to investigate the effect of line-edge roughness (LER)-induced random variation on the input/output transfer characteristics (e.g., off-state leakage current (Ioff), subthreshold slope (SS), saturation drain current (Id,sat), linear drain current (Id,lin), saturation threshold voltage (Vth,sat), and linear threshold voltage (Vth,lin)) of 5 nm FinFET. Hence, the prediction model was divided into two phases, i.e., “Predict Vth” and “Model Vth”. In the former, LER profiles were only used as training input features, and two threshold voltages (i.e., Vth,sat and Vth,lin) were target variables. In the latter, however, LER profiles and the two threshold voltages were used as training input features. The final prediction was then made by feeding the output of the first model to the input of the second model. The developed models were quantitatively evaluated by the Earth Mover Distance (EMD) between the target variables from the TCAD simulation tool and the predicted variables of the ANN model, and we confirm both the prediction accuracy and time-efficiency of our model.

scholarly journals Geographical origin identification of teas using UV-VIS spectroscopy

2021 ◽  
Vol 265 ◽  
pp. 05013
Author(s):  
Thi Hue Tran ◽  
Quoc Toan Tran ◽  
Thi Thao Ta ◽  
Si Hung Le
Keyword(s):  
Discriminant Analysis ◽  
Principal Component ◽  
Ann Model ◽  
Multivariate Statistical ◽  
Identification Rate ◽  
Tea Quality ◽  
Alternative Approach ◽  
Tea Infusions ◽  
Vis Spectroscopy ◽  
Artificial Neural Network Ann

In this work we proposed a method to verify the differentiating characteristics of simple tea infusions prepared in boiling water alone, which represents the final product as ingested by the consumers. For this purpose, total of 125 tea samples from different geographical provines of Vietnam have been analyzed in UV-Vis spectroscopy associated with multivariate statistical methods. Principal Component Analysis-Discriminant Analysis (PCA-DA), Partial Least Squares Discriminant Analysis (PLS-DA) and Artificial Neural Network (ANN) were compared to construct the identification model. The experimental results showed that the performance of ANN model was better than PCA-DA and PLS-DA model. The optimal ANN model was achieved when neuron numbers were 200, identification rate being 99% in the training set and 84% predition set. The proposed methodology provides a simpler, faster and more affordable classification of simple tea infusions, and can be used as an alternative approach to traditional tea quality evaluation.

scholarly journals Analysis of EMG Signals during Stance and Swing Phases for Controlling Magnetorheological Brake applications

2020 ◽  
Vol 11 (1) ◽  
pp. 112-119
Author(s):  
Nurhazimah Nazmi ◽  
Mohd Azizi Abdul Rahman ◽  
Saiful Amri Mazlan ◽  
Dimas Adiputra ◽  
Irfan Bahiuddin ◽  
...  
Keyword(s):  
Classification Performance ◽  
Visual Observation ◽  
Training Algorithms ◽  
Training Algorithm ◽  
Ann Model ◽  
Classification Rate ◽  
Time Domains ◽  
Artificial Neural Network Ann ◽  
Limb Rehabilitation ◽  
Better Than

AbstractThe development of ankle foot orthoses (AFO) for lower limb rehabilitation have received significant attention over the past decades. Recently, passive AFO equipped with magnetorheological brake had been developed based on ankle angle and electromyography (EMG) signals. Nonetheless, the EMG signals were categorized in stance and swing phases through visual observation as the signals are stochastic. Therefore, this study aims to classify the pattern of EMG signals during stance and swing phases. Seven-time domains features will be extracted and fed into artificial neural network (ANN) as a classifier. Two different training algorithms of ANN namely Levenberg-Marquardt (LM) and Scaled Conjugate Gradient (SCG) will be applied. As number of inputs will affect the classification performance of ANN, different number of input features will be employed. In this study, three participants were recruited and walk on the treadmills for 60 seconds by constant the speed. The ANN model was designed with 2, 10, 12, and 14 inputs features with LM and SCG training algorithms. Then, the ANN was trained ten times and the performances of each inputs features were measured using classification rate of training, testing, validation and overall. This study found that all the inputs with LM training algorithm gained more than 2% average classification rate than SCG training algorithm. On the other hand, classification accuracy of 10, 12 and 14 inputs were 5% higher than 2 inputs. It can be concluded that LM training algorithm of ANN was performed better than SCG algorithm with at least 10 inputs.

External aerodynamic investigation over Ahmed body for optimal topology selection between upper and under bodywork using ANN approach

2021 ◽  
Author(s):  
Virendra Talele ◽  
Nitish Karambali ◽  
Akshay Savekar ◽  
Sarthak Khatod ◽  
Sachin Pawar
Keyword(s):  
Drag Coefficient ◽  
Carbon Dioxide Emissions ◽  
Correlation Study ◽  
Primary Objective ◽  
Optimal Topology ◽  
Ann Model ◽  
Slant Angle ◽  
Post Process ◽  
Artificial Neural Network Ann ◽  
Ahmed Body

Aerodynamic improvements primarily result in decreased fuel usage and carbon dioxide emissions into the atmosphere. Numerous governments support ongoing aerodynamics development initiatives as a means of addressing the energy problem and reducing air pollution, Ahmed body investigation helps research to investigate versatile approaches and flexibility of design. This study is carried over a generic design of Ahmed body model. We attempted a passive arrangement system to reduce drag coefficient with a correlation of cases such as in primary objective varying parameter of slant angle from 20∘ to 30∘ proposed to monitor the behavior of drag coefficient. Once we finalized the optimum slant angle, which gives a lower drag coefficient, the next proposed configuration is to vary passive arrangement between lower and upper blend length to see the deflection of the boundary layer in correlation with the drag coefficient. The final topology is selected, which gives the lowest drag coefficient. The post-process correlation study was proposed by using an artificial neural network (ANN) scheme. The ANN model is developed between an achieved set of data from CFD investigation, ANN model indicates a strong correlation between the varying percentage of blend angle and increment percentage of the drag coefficient.

scholarly journals Hysteresis modeling of impact dynamics using artificial neural network

2021 ◽  
Vol 37 ◽  
pp. 333-338
Author(s):  
T A Tabaza ◽  
O Tabaza ◽  
J Barrett ◽  
A Alsakarneh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Impact Dynamics ◽  
Hysteresis Phenomenon ◽  
Data Sets ◽  
Ann Model ◽  
Hysteresis Modeling ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
The Impact

Abstract In this paper, the process of training an artificial neural network (ANN) on predicting the hysteresis of a viscoelastic ball and ash wood bat colliding system is discussed. To study how the material properties and the impact speed affect the hysteresis phenomenon, many experiments were conducted for colliding three types of viscoelastic balls known as sliotars at two different speeds. The aim of the study is to innovate a neural network model to predict the hysteresis phenomenon of the collision of viscoelastic materials. The model accurately captured the input data and was able to produce data sets out of the input ranges. The results show that the ANN model predicted the impact hysteresis accurately with <1% error.

scholarly journals Application of ANN to predict performance and emissions of SI engine using gasoline-methanol blends

2021 ◽  
Vol 104 (1) ◽  
pp. 003685042110023
Author(s):  
Ehtasham Ahmed ◽  
Muhammad Usman ◽  
Sibghatallah Anwar ◽  
Hafiz Muhammad Ahmad ◽  
Muhammad Waqar Nasir ◽  
...  
Keyword(s):  
Adaptive Learning ◽  
Alternative Fuels ◽  
Exhaust Emissions ◽  
Ann Model ◽  
Si Engine ◽  
Fuel Blends ◽  
Performance And Emissions ◽  
And Performance ◽  
Artificial Neural Network Ann ◽  
The Impact

The deployment of methanol like alternative fuels in engines is a necessity of the present time to comprehend power requirements and environmental pollution. Furthermore, a comprehensive prediction of the impact of the methanol-gasoline blend on engine characteristics is also required in the era of artificial intelligence. The current study analyzes and compares the experimental and Artificial Neural Network (ANN) aided performance and emissions of four-stroke, single-cylinder SI engine using methanol-gasoline blends of 0%, 3%, 6%, 9%, 12%, 15%, and 18%. The experiments were performed at engine speeds of 1300–3700 rpm with constant loads of 20 and 40 psi for seven different fractions of fuels. Further, an ANN model has developed setting fuel blends, speed and load as inputs, and exhaust emissions and performance parameters as the target. The dataset was randomly divided into three groups of training (70%), validation (15%), and testing (15%) using MATLAB. The feedforward algorithm was used with tangent sigmoid transfer active function (tansig) and gradient descent with an adaptive learning method. It was observed that the continuous addition of methanol up to 12% (M12) increased the performance of the engine. However, a reduction in emissions was observed except for NOx emissions. The regression correlation coefficient (R) and the mean relative error (MRE) were in the range of 0.99100–0.99832 and 1.2%–2.4% respectively, while the values of root mean square error were extremely small. The findings depicted that M12 performed better than other fractions. ANN approach was found suitable for accurately predicting the performance and exhaust emissions of small-scaled SI engines.

scholarly journals Ambient temperature change Vs confirmed cases of COVID-19: A machine learning model

2020 ◽  
Author(s):  
Amir Jamshidnezhad ◽  
Seyed Ahmad Hosseini ◽  
Leila Ibrahimi Ghavamabadi ◽  
Marzieh Zilae ◽  
Hediye Mousavi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Climatic Factors ◽  
Linear Regression Analysis ◽  
Ambient Air ◽  
Multiple Linear Regression Analysis ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Testing Stage ◽  
Artificial Neural Network Ann ◽  
The Impact

Abstract Background The relation between ambient air temperature and prevalence of viral infection has been under investigation in recent years. The present study aimed at providing the statistical and machine learning based analysis to investigate the influence of climatic factors on frequency of COVID-19 confirmed cases in Iran.Method The data of confirmed cases of COVID-19 as well as some climatic factors related to 31 provinces of Iran, during 04/03/2020 to 05/05/2020 were gathered from the official resources. In order to investigate the important climatic factors on the frequency of confirmed cases of Covid-19 in all studied cities, a model based on an artificial neural network (ANN) was developed. Moreover, the statistical analysis were used to assess the trend of positive cases in comparison with the fluctuations of some climatic factors. Results The proposed ANN model showed the accuracy rate of 87.25% and 86.4% in the training and testing stage, respectively for classification of COVID-19 confirmed cases. Moreover, multiple linear regression analysis was obtained the R2 equal to 0.40 and 0.68 in two cities of Qom and Ahvaz, respectively. The results showed that in the city of Ahvaz, despite the increase in temperature, the coefficient of determination R2 has been increasing. Conclusion This study clearly showed that with increasing outdoor temperature, the use of air conditioning systems to set a comfort zone temperature is unavoidable; thus the number of positive cases of COVID-19 increases. Also, this study shows the role of closed air cycle condition in indoor environment of tropical cities, along with the impact of climatic factors in the frequency of positive cases of COVID-19 and the capacity of ANN classification in the surveys.

Sign in / Sign up

Export Citation Format

Share Document