scholarly journals Deep Neural Networks to Detect Weeds from Crops in Agricultural Environments in Real-Time: A Review

2021 ◽  
Vol 13 (21) ◽  
pp. 4486
Author(s):  
Ildar Rakhmatuiln ◽  
Andreas Kamilaris ◽  
Christian Andreasen
Keyword(s):  
Machine Learning ◽  
Machine Vision ◽  
Learning Technologies ◽  
Machine Learning Techniques ◽  
Model Complex ◽  
Automation And Control ◽  
Classical Image ◽  
And Performance ◽  
Commercial Applications ◽  
And Control

Automation, including machine learning technologies, are becoming increasingly crucial in agriculture to increase productivity. Machine vision is one of the most popular parts of machine learning and has been widely used where advanced automation and control have been required. The trend has shifted from classical image processing and machine learning techniques to modern artificial intelligence (AI) and deep learning (DL) methods. Based on large training datasets and pre-trained models, DL-based methods have proven to be more accurate than previous traditional techniques. Machine vision has wide applications in agriculture, including the detection of weeds and pests in crops. Variation in lighting conditions, failures to transfer learning, and object occlusion constitute key challenges in this domain. Recently, DL has gained much attention due to its advantages in object detection, classification, and feature extraction. DL algorithms can automatically extract information from large amounts of data used to model complex problems and is, therefore, suitable for detecting and classifying weeds and crops. We present a systematic review of AI-based systems to detect weeds, emphasizing recent trends in DL. Various DL methods are discussed to clarify their overall potential, usefulness, and performance. This study indicates that several limitations obstruct the widespread adoption of AI/DL in commercial applications. Recommendations for overcoming these challenges are summarized.

scholarly journals Machine Learning for Benchmarking Models of Heating Energy Demand of Houses in Northern Canada

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1158
Author(s):  
Behrad Bezyan ◽  
Radu Zmeureanu
Keyword(s):  
Machine Learning ◽  
Energy Demand ◽  
Energy Use ◽  
Machine Learning Techniques ◽  
Northern Canada ◽  
Automation And Control ◽  
And Performance ◽  
Energy Signature ◽  
Heating Energy Demand ◽  
And Control

In most cases, the benchmarking models of energy use in houses are developed based on current and past data, and they continue to be used without any update. This paper proposes the method of retraining of benchmarking models by applying machine learning techniques when new measurements are made available. The method uses as a case study the measurements of heating energy demand from two semi-detached houses of Northern Canada. The results of the prediction of heating energy demand using static or augmented window techniques are compared with measurements. The daily energy signature is used as a benchmarking model due to its simplicity and performance. However, the proposed retraining method can be applied to any form of benchmarking model. The method should be applied in all possible situations, and be an integral part of intelligent building automation and control systems (BACS) for the ongoing commissioning for building energy-related applications.

Recognition System by Using Machine Vision Tools and Machine Learning Techniques for Mobile Robots

2021 ◽  
pp. 258-287
Author(s):  
Jesús Elias Miranda Vega ◽  
Anastacio González Chaidez ◽  
Cuauhtémoc Mariscal García ◽  
Moisés Rivas López ◽  
Wendy Flores Fuentes ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Vision ◽  
Mobile Robots ◽  
Human Activities ◽  
Work Conditions ◽  
Learning Technologies ◽  
Industrial Robots ◽  
Machine Learning Techniques ◽  
K Nearest Neighbors ◽  
Human Capabilities

The systems based on image recognition play an important role in many cases where inspection methods are critical for industrial processes. Machine vision is required in industry for monitoring and detecting objects in real-time applications. Industrial robots are increasingly used in a variety of industries and applications such as manipulators and mobile robots. These devices are necessary for dangerous work conditions and tasks that humans cannot do; however, these industrial robots can also do some human activities such as transport material, select, and identify objects. In order to substitute human capabilities is no easy task. Nevertheless, the use of artificial intelligence has been replacing some human activities for increased productivity. By supporting machine learning technologies, this chapter presents a k nearest neighbors' algorithm for image classification of mobile robots to detect and recognize objects.

scholarly journals Integration of image segmentation and fuzzy theory to improve the accuracy of damage detection areas in traffic accidents

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Majid Amirfakhrian ◽  
Mahboub Parhizkar
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Machine Vision ◽  
Traffic Accidents ◽  
Fuzzy Theory ◽  
Machine Learning Techniques ◽  
Industrial Setting ◽  
Technological Advances ◽  
Learning Techniques ◽  
Wide Range

AbstractIn the next decade, machine vision technology will have an enormous impact on industrial works because of the latest technological advances in this field. These advances are so significant that the use of this technology is now essential. Machine vision is the process of using a wide range of technologies and methods in providing automated inspections in an industrial setting based on imaging, process control, and robot guidance. One of the applications of machine vision is to diagnose traffic accidents. Moreover, car vision is utilized for detecting the amount of damage to vehicles during traffic accidents. In this article, using image processing and machine learning techniques, a new method is presented to improve the accuracy of detecting damaged areas in traffic accidents. Evaluating the proposed method and comparing it with previous works showed that the proposed method is more accurate in identifying damaged areas and it has a shorter execution time.

scholarly journals Automation and control of laser wakefield accelerators using Bayesian optimization

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
R. J. Shalloo ◽  
S. J. D. Dann ◽  
J.-N. Gruse ◽  
C. I. D. Underwood ◽  
A. F. Antoine ◽  
...  
Keyword(s):  
Pulse Length ◽  
Bayesian Optimization ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Laser Wakefield ◽  
Accelerating Structure ◽  
Automation And Control ◽  
Laser Pulse Shape ◽  
And Control ◽  
Wakefield Accelerators

AbstractLaser wakefield accelerators promise to revolutionize many areas of accelerator science. However, one of the greatest challenges to their widespread adoption is the difficulty in control and optimization of the accelerator outputs due to coupling between input parameters and the dynamic evolution of the accelerating structure. Here, we use machine learning techniques to automate a 100 MeV-scale accelerator, which optimized its outputs by simultaneously varying up to six parameters including the spectral and spatial phase of the laser and the plasma density and length. Most notably, the model built by the algorithm enabled optimization of the laser evolution that might otherwise have been missed in single-variable scans. Subtle tuning of the laser pulse shape caused an 80% increase in electron beam charge, despite the pulse length changing by just 1%.

scholarly journals Malicious web domain identification using online credibility and performance data by considering the class imbalance issue

2019 ◽  
Vol 119 (3) ◽  
pp. 676-696 ◽  
Author(s):  
Zhongyi Hu ◽  
Raymond Chiong ◽  
Ilung Pranata ◽  
Yukun Bao ◽  
Yuqing Lin
Keyword(s):  
Machine Learning ◽  
Class Imbalance ◽  
Performance Data ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Real World Data ◽  
Content Type ◽  
Domain Identification ◽  
Learning Techniques ◽  
And Performance

Purpose Malicious web domain identification is of significant importance to the security protection of internet users. With online credibility and performance data, the purpose of this paper to investigate the use of machine learning techniques for malicious web domain identification by considering the class imbalance issue (i.e. there are more benign web domains than malicious ones). Design/methodology/approach The authors propose an integrated resampling approach to handle class imbalance by combining the synthetic minority oversampling technique (SMOTE) and particle swarm optimisation (PSO), a population-based meta-heuristic algorithm. The authors use the SMOTE for oversampling and PSO for undersampling. Findings By applying eight well-known machine learning classifiers, the proposed integrated resampling approach is comprehensively examined using several imbalanced web domain data sets with different imbalance ratios. Compared to five other well-known resampling approaches, experimental results confirm that the proposed approach is highly effective. Practical implications This study not only inspires the practical use of online credibility and performance data for identifying malicious web domains but also provides an effective resampling approach for handling the class imbalance issue in the area of malicious web domain identification. Originality/value Online credibility and performance data are applied to build malicious web domain identification models using machine learning techniques. An integrated resampling approach is proposed to address the class imbalance issue. The performance of the proposed approach is confirmed based on real-world data sets with different imbalance ratios.

scholarly journals Neuromorphic Computation With a Single Magnetic Domain Wall

2021 ◽  
Author(s):  
Razvan V. Ababei ◽  
Matthew O. A. Ellis ◽  
Ian T. Vidamour ◽  
Dhilan S. Devadasan ◽  
Dan A. Allwood ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Machine Learning ◽  
Domain Wall ◽  
Domain Walls ◽  
Magnetic Domain ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Complex Data ◽  
Reservoir Computing ◽  
Model Complex

Abstract Machine learning techniques are commonly used to model complex relationships but implementations on digital hardware are relatively inefficient due to poor matching between conventional computer architectures and the structures of the algorithms they are required to simulate. Neuromorphic devices, and in particular reservoir computing architectures, utilize the inherent properties of physical systems to implement machine learning algorithms and so have the potential to be much more efficient. In this work, we demonstrate that the dynamics of individual domain walls in magnetic nanowires are suitable for implementing the reservoir computing paradigm in hardware. We modelled the dynamics of a domain wall placed between two anti-notches in a nickel nanowire using both a 1d collective coordinates model and micromagnetic simulations. When driven by an oscillating magnetic field, the domain exhibits non-linear dynamics within the potential well created by the anti-notches that are analogous to those of the Duffing oscillator. We exploit the domain wall dynamics for reservoir computing by modulating the amplitude of the applied magnetic field to inject time-multiplexed input signals into the reservoir, and show how this allows us to perform machine learning tasks including: the classification of (1) sine and square waves; (2) spoken digits and (3) non-temporal 2D toy data and hand written digits. Our work lays the foundation for the creation of nanoscale neuromorphic devices in which individual magnetic domain walls are used to perform complex data analysis tasks.

scholarly journals A Review on Machine Learning Models in Injection Molding Machines

2022 ◽  
Vol 2022 ◽  
pp. 1-28
Author(s):  
Senthil Kumaran Selvaraj ◽  
Aditya Raj ◽  
R. Rishikesh Mahadevan ◽  
Utkarsh Chadha ◽  
Velmurugan Paramasivam
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Injection Molding ◽  
Performance Criteria ◽  
Machine Learning Techniques ◽  
Injection Molding Process ◽  
Molding Process ◽  
Injection Speed ◽  
Detection Techniques ◽  
And Performance

One of the most suitable methods for the mass production of complicated shapes is injection molding due to its superior production rate and quality. The key to producing higher quality products in injection molding is proper injection speed, pressure, and mold design. Conventional methods relying on the operator’s expertise and defect detection techniques are ineffective in reducing defects. Hence, there is a need for more close control over these operating parameters using various machine learning techniques. Neural networks have considerable applications in the injection molding process consisting of optimization, prediction, identification, classification, controlling, modeling, and monitoring, particularly in manufacturing. In recent research, many critical issues in applying machine learning and neural network in injection molding in practical have been addressed. Some problems include data division, collection, and preprocessing steps, such as considering the inputs, networks, and outputs, algorithms used, models utilized for testing and training, and performance criteria set during validation and verification. This review briefly explains working on machine learning and artificial neural network and optimizing injection molding in industries.

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