Modeling of Basil Leaves Drying by GA–ANN

2013 ◽  
Vol 9 (4) ◽  
pp. 393-401 ◽  
Author(s):  
Amin Taheri-Garavand ◽  
Shahin Rafiee ◽  
Alireza Keyhani ◽  
Payam Javadikia
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Relative Humidity ◽  
Moisture Content ◽  
Air Temperature ◽  
High Accuracy ◽  
Air Velocity ◽  
Feed Forward Neural Network ◽  
Air Temperatures ◽  
Hidden Layer

AbstractIn this research, the experiment is done by a dryer. It could provide any desired drying air temperature between 20 and 120°C and air relative humidity between 5 and 95% and air velocity between 0.1 and 5.0 m/s with high accuracy, and the drying experiment was conducted at five air temperatures of 40, 50, 60, 70 and 80°C and at three relative humidity 20, 40 and 60% and air velocity of 1.5, 2 and 2.5 m/s to dry Basil leaves. Then with developed Program in MATLAB software and by Genetic Algorithm could find the best Feed-Forward Neural Network (FFNN) structure to model the moisture content of dried Basil in each condition; anyway the result of best network by GA had only one hidden layer with 11 neurons. This network could predict moisture content of dried basil leaves with correlation coefficient of 0.99.

Genetic Programming for Letters Identification Based on Neural Network

2015 ◽  
Vol 734 ◽  
pp. 642-645
Author(s):  
Yan Hui Liu ◽  
Zhi Peng Wang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Genetic Programming ◽  
Network Structure ◽  
High Accuracy ◽  
Identification System ◽  
Neural Network Structure ◽  
Hidden Layer

According to the problem that the letters identification is not high accuracy using neural networks, in this paper, an optimal neural network structure is designed based on genetic algorithm to optimize the number of hidden layer. The English letters can be identified by optimal neural network. The results obtained in the genetic programming optimizations are very satisfactory. Experiments show that the identification system has higher accuracy and achieved good ideal letters identification effect.

scholarly journals Impact of the measurement uncertainty on the monitoring of thermal comfort through AI predictive algorithms

ACTA IMEKO ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 221
Author(s):  
Nicole Morresi ◽  
Sara Casaccia ◽  
Marco Arnesano ◽  
Gian Marco Revel
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Relative Humidity ◽  
Measurement Uncertainty ◽  
Thermal Comfort ◽  
Air Temperature ◽  
Air Velocity ◽  
Human Thermal Comfort ◽  
Innovative Method ◽  
Predictive Algorithms

This paper presents an approach to assess the measurement uncertainty of human thermal comfort by using an innovative method that comprises a heterogeneous set of data, made by physiological and environmental quantities, and artificial intelligence algorithms, using Monte Carlo method (MCM). The dataset is made up of heart rate variability (HRV) features, air temperature, air velocity and relative humidity. Firstly, MCM is applied to compute the measurement uncertainty of the HRV features: results have shown that among 13 participants, there are uncertainty values in the measurement of HRV features that ranges from ±0.01% to ±0.7 %, suggesting that the uncertainty can be generalized among different subjects. Secondly, MCM is applied by perturbing the input parameters of random forest (RF) and convolutional neural network (CNN) algorithm, trained to measure human thermal comfort. Results show that environmental quantities produce different uncertainty on the thermal comfort: RF has the highest uncertainty due to the air temperature (14 %), while CNN has the highest uncertainty when relative humidity is perturbed (10.5 %). A sensitivity analysis also shows that air velocity is the parameter that causes a higher deviation of thermal comfort

Modelling and analysis of compressive strength properties of parboiled paddy and milled rice

2014 ◽  
Vol 28 (1) ◽  
pp. 73-83 ◽  
Author(s):  
Abozar Nasirahmadi ◽  
Mohammad H. Abbaspour-Fard ◽  
Bagher Emadi ◽  
Nasser Behroozi Khazaei
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Compressive Strength ◽  
Moisture Content ◽  
Mean Squared Error ◽  
Strength Properties ◽  
Milled Rice ◽  
Artificial Neural ◽  
Hidden Layer

Abstract The present investigation deals with analyzing the compressive strength properties of two varieties (Tarom and Fajr) of parboiled paddy and milled rice including: ultimate stress, modulus of elasticity, rupture force and rupture energy. Combined artificial neural network and genetic algorithm were also applied to model these properties. The parboiled samples were prepared with three soaking temperatures (25, 50 and 75°C) and three steaming times (10, 15 and 20 min). The samples were then dried to final moisture contents of 8, 10 and 12% (w.b.). In general, Tarom variety had higher compressive strength properties for paddy and milled rice than Fajr variety. With increase in steaming time from 10 to 20 min, all mentioned properties increased significantly, whereas these properties were decreased with increasing moisture content from 8 to 12% (w.b.). Coupled artificial neural network and genetic algorithm model with one hidden layer, three inputs (soaking temperature, steaming time and moisture content), was developed to predict the compressive strength properties as model outputs. Results indicated that this model could predict these properties with high correlation and low mean squared error.

scholarly journals Artificial Neural Network for the Thermal Comfort Index Prediction: Development of a New Simplified Algorithm

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4500
Author(s):  
Domenico Palladino ◽  
Iole Nardi ◽  
Cinzia Buratti
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Relative Humidity ◽  
Thermal Comfort ◽  
Air Temperature ◽  
Thermal Conditions ◽  
Feed Forward Neural Network ◽  
Clothing Insulation ◽  
Artificial Neural ◽  
Simplified Algorithm

A simplified algorithm using an artificial neural network (ANN, a feed-forward neural network) for the assessment of the predicted mean vote (PMV) index in summertime was developed, using solely three input variables (namely the indoor air temperature, relative humidity, and clothing insulation), whilst low air speed (<0.1 m/s), a minimal variation of radiant temperature (25.1 °C ± 2 °C) and steady metabolism (1.2 Met) were considered. Sensitivity analysis to the number of variables and to the number of neurons were performed. The developed ANN was then compared with three proven methods used for thermal comfort prediction: (i) the International Standard; (ii) the Rohles model; (iii) the modified Rohles model. Finally, another network able to predict the indoor thermal conditions was considered: the combined calculation of the two networks was tested for the PMV prediction. The proposed algorithm allows one to better approximate the PMV index than the other models (mean error of ANN predominantly in ±0.10–±0.20 range). The accuracy of the network in PMV prediction increases when air temperature and relative humidity values fall into 21–28 °C and 30–75% ranges. When the PMV is predicted by using the combined calculation (i.e., by using the two networks), the same order of magnitude of error was found, confirming the reliability of the networks. The developed ANN could be considered as an alternative method for the simplified prediction of PMV; moreover, the new simplified algorithm can be useful in buildings’ design phase, i.e., in those cases where experimental data are not available.

scholarly journals Quantifying the Independent Influences of Land Cover and Humidity on Microscale Urban Air Temperature Variation in Hot Summer: Methods of Path Analysis and Genetic SVR

Atmosphere ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1377
Author(s):  
Weifang Shi ◽  
Nan Wang ◽  
Aixuan Xin ◽  
Linglan Liu ◽  
Jiaqi Hou ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Land Cover ◽  
Path Analysis ◽  
Temperature Variation ◽  
Direct Effect ◽  
Air Temperature ◽  
Urban Areas ◽  
Support Vector ◽  
Urban Air ◽  
Air Temperatures

Mitigating high air temperatures and heat waves is vital for decreasing air pollution and protecting public health. To improve understanding of microscale urban air temperature variation, this paper performed measurements of air temperature and relative humidity in a field of Wuhan City in the afternoon of hot summer days, and used path analysis and genetic support vector regression (SVR) to quantify the independent influences of land cover and humidity on air temperature variation. The path analysis shows that most effect of the land cover is mediated through relative humidity difference, more than four times as much as the direct effect, and that the direct effect of relative humidity difference is nearly six times that of land cover, even larger than the total effect of the land cover. The SVR simulation illustrates that land cover and relative humidity independently contribute 16.3% and 83.7%, on average, to the rise of the air temperature over the land without vegetation in the study site. An alternative strategy of increasing the humidity artificially is proposed to reduce high air temperatures in urban areas. The study would provide scientific support for the regulation of the microclimate and the mitigation of the high air temperature in urban areas.

Thermal Lethality of Salmonella in Chicken Leg Quarters Processed via an Air/Steam Impingement Oven

2004 ◽  
Vol 67 (3) ◽  
pp. 493-498 ◽  
Author(s):  
R. Y. MURPHY ◽  
K. H. DRISCOLL ◽  
L. K. DUNCAN ◽  
T. OSAILI ◽  
J. A. MARCY
Keyword(s):  
Relative Humidity ◽  
Surface Area ◽  
Air Temperature ◽  
Cooking Time ◽  
Air Velocity ◽  
Poultry Products ◽  
Product Surface ◽  
Cooking Times

Chicken leg quarters were injected with 0.1 ml of the cocktail culture per cm2 of the product surface area to contain about 7 log(CFU/g) of Salmonella. The inoculated leg quarters were processed in an air/steam impingement oven at an air temperature of 232°C, an air velocity of 1.4 m/s, and a relative humidity of 43%. The endpoint product temperatures were correlated with the cooking times. A model was developed for pathogen thermal lethality up to 7 log(CFU/g) reductions of Salmonella in correlation to the product mass (140 to 540 g) and cooking time (5 to 35 min). The results from this study are useful for validating thermal lethality of pathogens in poultry products that are cooked via impingement ovens.

HIERARCHICAL GENETIC ALGORITHM FOR NEAR-OPTIMAL FEEDFORWARD NEURAL NETWORK DESIGN

2002 ◽  
Vol 12 (01) ◽  
pp. 31-43 ◽  
Author(s):  
GARY YEN ◽  
HAIMING LU
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Cost Function ◽  
Design Procedure ◽  
Optimal Solution ◽  
Traditional Learning ◽  
Feed Forward Neural Network ◽  
Optimal Solution Set ◽  
Hierarchical Genetic Algorithm

In this paper, we propose a genetic algorithm based design procedure for a multi-layer feed-forward neural network. A hierarchical genetic algorithm is used to evolve both the neural network's topology and weighting parameters. Compared with traditional genetic algorithm based designs for neural networks, the hierarchical approach addresses several deficiencies, including a feasibility check highlighted in literature. A multi-objective cost function is used herein to optimize the performance and topology of the evolved neural network simultaneously. In the prediction of Mackey–Glass chaotic time series, the networks designed by the proposed approach prove to be competitive, or even superior, to traditional learning algorithms for the multi-layer Perceptron networks and radial-basis function networks. Based upon the chosen cost function, a linear weight combination decision-making approach has been applied to derive an approximated Pareto-optimal solution set. Therefore, designing a set of neural networks can be considered as solving a two-objective optimization problem.

scholarly journals High-accuracy motion control of a motor servo system with dead-zone based on a single hidden layer neural network

2020 ◽  
Vol 30 (1) ◽  
pp. 27-44
Author(s):  
Jian Hu ◽  
Shupeng Cao ◽  
Chenchen Xu ◽  
Jianyong Yao ◽  
Zhiwei Xie
Keyword(s):  
Neural Network ◽  
Motion Control ◽  
Dead Zone ◽  
Servo System ◽  
High Accuracy ◽  
Hidden Layer

scholarly journals A Study on Suitable Habitat for Swiftlet Farming

2009 ◽  
Vol 1 (1) ◽  
pp. 1-7
Author(s):  
Ibrahim S. H. ◽  
Teo W.C. ◽  
Baharun A.
Keyword(s):  
Relative Humidity ◽  
Light Intensity ◽  
Air Temperature ◽  
Capital Investment ◽  
Suitable Habitat ◽  
Key Factors ◽  
Air Velocity ◽  
Animal Products ◽  
New Industry ◽  
Temperature Surface

Swiftlet farming is a new industry in Sarawak as compared to other long-standing industries such as rubber, palm oil and timber. It is one of the businesses that involved a small capital investment that could generate enormous returns in the future. Swiftlet farming involves the conversion of human-centric building into structures for Swiftlet. The purpose of this conversion is to let Swiftlet for nesting and protect them. The design and construction of such building will also helps to accommodate Swiftlets' population. The nest of the Edible-nest Swiftlet rank amongst the world's most expensive animal products. Therefore, in order to increase the productivity of bird nest, study of the suitable habitat for Swiftlet should be done thoroughly. Environmental factors such as air temperature, surface temperature, relative humidity, air velocity and light intensity are the key factors for a successful Swiftlet farm house. Internal air temperature of building should be maintained from 26°C to 35°C, relative humidity from 80% to 90%, low air velocity and light intensity less than 5 LUX. Proper ventilation and installation of a humidifier could help the building to achieve the desirable range of environment factors. Location of structure will also be considered from direct sunlight direction to reduce the internal temperature. Only licensed Swiftlet farming is legal.

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