scholarly journals Computer Vision and Machine Learning Analysis of Commercial Rice Grains: A Potential Digital Approach for Consumer Perception Studies

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6354
Author(s):  
Aimi Aznan ◽  
Claudia Gonzalez Viejo ◽  
Alexis Pang ◽  
Sigfredo Fuentes
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Imaging System ◽  
Cost Effective ◽  
Consumer Perception ◽  
Rice Grain ◽  
Ann Model ◽  
Practical Applications ◽  
Rice Samples ◽  
Artificial Neural Network Ann

Rice quality assessment is essential for meeting high-quality standards and consumer demands. However, challenges remain in developing cost-effective and rapid techniques to assess commercial rice grain quality traits. This paper presents the application of computer vision (CV) and machine learning (ML) to classify commercial rice samples based on dimensionless morphometric parameters and color parameters extracted using CV algorithms from digital images obtained from a smartphone camera. The artificial neural network (ANN) model was developed using nine morpho-colorimetric parameters to classify rice samples into 15 commercial rice types. Furthermore, the ANN models were deployed and evaluated on a different imaging system to simulate their practical applications under different conditions. Results showed that the best classification accuracy was obtained using the Bayesian Regularization (BR) algorithm of the ANN with ten hidden neurons at 91.6% (MSE = <0.01) and 88.5% (MSE = 0.01) for the training and testing stages, respectively, with an overall accuracy of 90.7% (Model 2). Deployment also showed high accuracy (93.9%) in the classification of the rice samples. The adoption by the industry of rapid, reliable, and accurate methods, such as those presented here, may allow the incorporation of different morpho-colorimetric traits in rice with consumer perception studies.

scholarly journals Modeling Fused Filament Fabrication using Artificial Neural Networks

2021 ◽  
Author(s):  
Paul Oehlmann ◽  
Paul Osswald ◽  
Juan Camilo Blanco ◽  
Martin Friedrich ◽  
Dominik Rietzel ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Cost Effective ◽  
Print Quality ◽  
Ann Model ◽  
Production Environment ◽  
Fused Filament Fabrication ◽  
Artificial Neural ◽  
Using Data ◽  
Artificial Neural Network Ann

AbstractWith industries pushing towards digitalized production, adaption to expectations and increasing requirements for modern applications, has brought additive manufacturing (AM) to the forefront of Industry 4.0. In fact, AM is a main accelerator for digital production with its possibilities in structural design, such as topology optimization, production flexibility, customization, product development, to name a few. Fused Filament Fabrication (FFF) is a widespread and practical tool for rapid prototyping that also demonstrates the importance of AM technologies through its accessibility to the general public by creating cost effective desktop solutions. An increasing integration of systems in an intelligent production environment also enables the generation of large-scale data to be used for process monitoring and process control. Deep learning as a form of artificial intelligence (AI) and more specifically, a method of machine learning (ML) is ideal for handling big data. This study uses a trained artificial neural network (ANN) model as a digital shadow to predict the force within the nozzle of an FFF printer using filament speed and nozzle temperatures as input data. After the ANN model was tested using data from a theoretical model it was implemented to predict the behavior using real-time printer data. For this purpose, an FFF printer was equipped with sensors that collect real time printer data during the printing process. The ANN model reflected the kinematics of melting and flow predicted by models currently available for various speeds of printing. The model allows for a deeper understanding of the influencing process parameters which ultimately results in the determination of the optimum combination of process speed and print quality.

New recommendation to predict export value using big data and machine learning technique

2021 ◽  
pp. 1-14
Author(s):  
Rani Nooraeni ◽  
Jimmy Nickelson ◽  
Eko Rahmadian ◽  
Nugroho Puspito Yudho
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Big Data ◽  
Ann Model ◽  
Machine Learning Technique ◽  
Current Period ◽  
Learning Technique ◽  
Monthly Period ◽  
Conventional Machine ◽  
Artificial Neural Network Ann

Official statistics on monthly export values have a publicity lag between the current period and the published publication. None of the previous researchers estimated the value of exports for the monthly period. This circumstance is due to limitations in obtaining supporting data that can predict the criteria for the current export value of goods. AIS data is one type of big data that can provide solutions in producing the latest indicators to forecast export values. Statistical Methods and Conventional Machine Learning are implemented as forecasting methods. Seasonal ARIMA and Artificial Neural Network (ANN) methods are both used in research to forecast the value of Indonesia’s exports. However, ANN has a weakness that requires high computational costs to obtain optimal parameters. Genetic Algorithm (GA) is effective in increasing ANN accuracy. Based on these backgrounds, this paper aims to develop and select an AIS indicator to predict the monthly export value in Indonesia and optimize ANN performance by combining the ANN algorithm with the genetic algorithm (GA-ANN). The research successfully established five indicators that can be used as predictors in the forecasting model. According to the model evaluation results, the genetic algorithm has succeeded in improving the performance of the ANN model as indicated by the resulting RMSE GA-ANN value, which is smaller than the RMSE of the ANN model.

scholarly journals Cost-Effective CNNs for Real-Time Micro-Expression Recognition

2020 ◽  
Vol 10 (14) ◽  
pp. 4959
Author(s):  
Reda Belaiche ◽  
Yu Liu ◽  
Cyrille Migniot ◽  
Dominique Ginhac ◽  
Fan Yang
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Real Time ◽  
Video Clip ◽  
Cost Effective ◽  
Compact Representation ◽  
Expression Recognition ◽  
Memory Space ◽  
Computational Resources ◽  
Micro Expression

Micro-Expression (ME) recognition is a hot topic in computer vision as it presents a gateway to capture and understand daily human emotions. It is nonetheless a challenging problem due to ME typically being transient (lasting less than 200 ms) and subtle. Recent advances in machine learning enable new and effective methods to be adopted for solving diverse computer vision tasks. In particular, the use of deep learning techniques on large datasets outperforms classical approaches based on classical machine learning which rely on hand-crafted features. Even though available datasets for spontaneous ME are scarce and much smaller, using off-the-shelf Convolutional Neural Networks (CNNs) still demonstrates satisfactory classification results. However, these networks are intense in terms of memory consumption and computational resources. This poses great challenges when deploying CNN-based solutions in many applications, such as driver monitoring and comprehension recognition in virtual classrooms, which demand fast and accurate recognition. As these networks were initially designed for tasks of different domains, they are over-parameterized and need to be optimized for ME recognition. In this paper, we propose a new network based on the well-known ResNet18 which we optimized for ME classification in two ways. Firstly, we reduced the depth of the network by removing residual layers. Secondly, we introduced a more compact representation of optical flow used as input to the network. We present extensive experiments and demonstrate that the proposed network obtains accuracies comparable to the state-of-the-art methods while significantly reducing the necessary memory space. Our best classification accuracy was 60.17% on the challenging composite dataset containing five objectives classes. Our method takes only 24.6 ms for classifying a ME video clip (less than the occurrence time of the shortest ME which lasts 40 ms). Our CNN design is suitable for real-time embedded applications with limited memory and computing resources.

scholarly journals Material Classification via Machine Learning Techniques: Construction Projects Progress Monitoring

2021 ◽  
Author(s):  
Wesam Salah Alaloul ◽  
Abdul Hannan Qureshi
Keyword(s):  
Machine Learning ◽  
Construction Projects ◽  
Industrial Revolution ◽  
Progress Monitoring ◽  
Concrete Block ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Ann Model ◽  
Material Classification ◽  
Artificial Neural Network Ann

Nowadays, the construction industry is on a fast track to adopting digital processes under the Industrial Revolution (IR) 4.0. The desire to automate maximum construction processes with less human interference has led the industry and research community to inclined towards artificial intelligence. This chapter has been themed on automated construction monitoring practices by adopting material classification via machine learning (ML) techniques. The study has been conducted by following the structure review approach to gain an understanding of the applications of ML techniques for construction progress assessment. Data were collected from the Web of Science (WoS) and Scopus databases, concluding 14 relevant studies. The literature review depicted the support vector machine (SVM) and artificial neural network (ANN) techniques as more effective than other ML techniques for material classification. The last section of this chapter includes a python-based ANN model for material classification. This ANN model has been tested for construction items (brick, wood, concrete block, and asphalt) for training and prediction. Moreover, the predictive ANN model results have been shared for the readers, along with the resources and open-source web links.

scholarly journals Machine Learning Based Methods in Computational Micromechanics of Composite Materials

2021 ◽  
Vol 2 (3) ◽  
pp. 559-570
Author(s):  
Kian K. Sepahvand
Keyword(s):  
Machine Learning ◽  
Input Parameter ◽  
Hermite Polynomials ◽  
Supervised Machine Learning ◽  
Fe Model ◽  
Ann Model ◽  
Effective Parameters ◽  
Elastic Parameters ◽  
Element Analysis ◽  
Artificial Neural Network Ann

Design of new materials is quite a difficult task owing to various time and length scales and affiliated uncertainties. The major challenge is to include all these in a conventional model. Hyperparameter models in machine learning can be used to overcome these issues. In this paper, an artificial neural network (ANN) model is developed to estimate the effective elastic parameters of unidirectional fiber reinforced composites using representative volume elements (RVE) considering uncertainty in the fiber diameter. The diameter probability distribution is constructed from the acquired gray images by employing image processing operations. The generalized Polynomial Chaos (gPC) expansion is then used to represent the distribution as a random input parameter for finite element analysis, from where the effective parameters are realized. Similarly, the outputs of the FE model, i.e., elastic parameters, are approximated by gPC expansions having unknown deterministic coefficients and random orthogonal Hermite polynomials. A set of collocation points are generated from roots of the random polynomials; from there, the unknown coefficients are estimated. The realization samples are utilized to train an ANN algorithm based on supervised machine learning. The developed ANN model is later tested and validated for a new sample set of data. It is shown that the ANN model with few hidden layers and neurons has a high accuracy for estimation of the elastic parameters directly from the information on the distribution of fiber diameters.

scholarly journals Designing a Compact Microstrip Antenna Using the Machine Learning Approach

2020 ◽  
Vol 4 ◽  
pp. 44-52
Author(s):  
Kanhaiya Sharma ◽  
Ganga Prasad Pandey
Keyword(s):  
Machine Learning ◽  
Microstrip Antenna ◽  
Machine Learning Techniques ◽  
Dual Band ◽  
Ann Model ◽  
Shape Prediction ◽  
Learning Techniques ◽  
Notch Length ◽  
Artificial Neural Network Ann ◽  
Rectangular Microstrip Antenna

This paper presents how machine learning techniques may be applied in the process of designing a compact dual-band H-shaped rectangular microstrip antenna (RMSA) operating in 0.75–2.20 GHz and 3.0–3.44 GHz frequency ranges. In the design process, the same dimensions of upper and lower notches are incorporated, with the centered position right in the middle. Notch length and width are verified for investigating the antenna. An artificial neural network (ANN) model is developed from the simulated dataset, and is used for shape prediction. The same dataset is used to create a mathematical model as well. The predicted outcome is compared and it is determined that the model relying on ANN offers better results

Determining Rig State from Computer Vision Analytics

2021 ◽  
Author(s):  
Crispin Chatar ◽  
Suhas Suresha ◽  
Laetitia Shao ◽  
Soumya Gupta ◽  
Indranil Roychoudhury
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Graphics Processing Units ◽  
New Technology ◽  
Three Dimensional ◽  
Cost Effective ◽  
Temporal Information ◽  
Sensor Data ◽  
High Tech ◽  
Drilling Rig

Abstract For years, many companies involved with drilling have searched for the ideal method to calculate the state of a drilling rig. While companies cannot agree on a standard definition of "rig state," they can agree that as we move forward in drilling optimization and with further use of remote operations and automation, that rig state calculation is mandatory in one form or the other. Internally in the service company, many methods exist for calculating rig state, but one new technology area holds promise to deliver a more efficient and cost-effective option with higher accuracy. This technology involves vision analytics. Currently, detection algorithms rely heavily on data collected by sensors installed on the rig. However, relying exclusively on sensor data is problematic because sensors are prone to failure and are expensive to maintain and install. By proposing a machine learning model that relies exclusively on videos collected on the rig floor to infer rig states, it is possible to move away from the existing methods as the industry moves to a future of high-tech rigs. Videos, in contrast to sensor data, are relatively easy to collect from small inexpensive cameras installed at strategic locations. Consequently, this paper presents machine learning pipeline that is implemented to perform rig state determination from videos captured on the rig floor of an operating rig. The pipeline can be described in two parts. Firstly, the annotation pipeline matches each frame of the video dataset to a rig state. A convolutional neural network (CNN) is used to match the time of the video with corresponding sensor data. Secondly, additional CNNs are trained, capturing both spatial and temporal information, to extract an estimation of rig state from videos. The models are trained on a dataset of 3 million frames on a cloud platform using graphics processing units (GPU). Some of the models used include a pretrained visual geometry group (VGG) network, a convolutional three-dimensional (C3D) model that used three-dimensional (3D) convolutions, and a two-stream model that uses optical flow to capture temporal information. The initial results demonstrate this pipeline to be effective in detecting rig states using computer vision analytics.

scholarly journals The role of ambient parameters on transmission rates of the COVID-19 outbreak: A machine learning model

Work ◽  
2021 ◽  
pp. 1-9
Author(s):  
Amir Jamshidnezhad ◽  
Seyed Ahmad Hosseini ◽  
Leila Ibrahimi Ghavamabadi ◽  
Seyed Mahdi Hossaeini Marashi ◽  
Hediye Mousavi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Climatic Factors ◽  
Ambient Air ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Machine Learning Model ◽  
Testing Stage ◽  
Artificial Neural Network Ann ◽  
Air Conditioning Systems

BACKGROUND: In recent years the relationship between ambient air temperature and the prevalence of viral infection has been under investigation. OBJECTIVE: The study was 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 and some climatic factors related to 31 provinces of Iran between 04/03/2020 and 05/05/2020 was gathered from 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. RESULTS: The proposed ANN model showed accuracy rates of 87.25%and 86.4%in the training and testing stage, respectively, for classification of COVID-19 confirmed cases. 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 the indoor environment of tropical cities.

A Non-Linear Auto-Regressive With Exogenous Inputs (NARX) Artificial Neural Network (ANN) Model for Building Thermal Load Prediction

2019 ◽  
Author(s):  
Byeongho Yu ◽  
Dongsu Kim ◽  
Heejin Cho ◽  
Pedro Mago
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Nonlinear System ◽  
Prediction Model ◽  
Thermal Load ◽  
Ann Model ◽  
Load Prediction ◽  
Non Linear ◽  
Artificial Neural Network Ann

Abstract Thermal load prediction is a key part of energy system management and control in buildings, and its accuracy plays a critical role to improve and maintain building energy performance and efficiency. To address this issue, various types of prediction model have been considered and studied, such as physics-based, statistical, and machine learning models. Physical models can be accurate but require extended lead time for model development. Statistical models are relatively simple to develop and require less computation time than other models, but they may not provide accurate results for complex energy systems with an intricate nonlinear dynamic behavior. This study proposes an Artificial Neural Network (ANN) model, one of the prevalent machine learning methods to predict building thermal load, combining with the concept of Non-linear Auto-Regression with Exogenous inputs (NARX). NARX-ANN prediction model is distinguished from typical ANN models due to the fact that the NARX concept can address nonlinear system behaviors effectively based on recurrent architectures and time indexing features. To examine the suitability and validity of NARX-ANN model for building thermal load prediction, a case study is carried out using field data of an academic campus building at Mississippi State University. Results show that the proposed NARX-ANN model can provide an accurate prediction performance and effectively address nonlinear system behaviors in the prediction.

scholarly journals An Electrical Capacitance Array for Imaging of Water Leakage inside Insulating Slabs with Porous Cells

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2514 ◽  
Author(s):  
Rui Li ◽  
Yi Li ◽  
Lihui Peng
Keyword(s):  
Sensor Array ◽  
Imaging System ◽  
Electrical Potential ◽  
Cost Effective ◽  
Capacitance Measurement ◽  
Total Variation Regularization ◽  
Capacitance Sensor ◽  
Water Leakage ◽  
Test Setup ◽  
Practical Applications

The paper proposes a capacitance-sensor-array-based imaging system to detect water leakage inside insulating slabs with porous cells, such as anechoic acoustic rubber tiles. The modeling is conducted by using the finite element method to obtain the electrical potential distribution and sensitivity map with the proposed capacitance sensor array. An experimental test setup, which is composed of an eight-electrode capacitance sensor array and a commercialized capacitance bridge instrument for measurement, is developed. Experiments regarding different leakage scenarios are carried out by using the test setup. Preliminary results standing for different water leakage cases, which are based on the experimental data obtained from the test setup, are presented and depicted as images reconstructed by using different algorithms including the linear back projection (LBP), the projected Landweber iteration, and the total variation regularization. These results demonstrate that the proposed capacitance sensor array is feasible and has a great potential for imaging of water leakage inside insulating slabs with porous cells. A cost-effective capacitance measurement circuit for practical applications is also proposed and simulated.

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