Fuzzy control and neural networks industrial applications in the world of PLCs

1994 ◽  
Author(s):  
Wegmann
Keyword(s):  
Neural Networks ◽  
Fuzzy Control ◽  
Industrial Applications ◽  
The World

scholarly journals Deep Convolutional Neural Network with RNNs for Complex Activity Recognition Using Wrist-Worn Wearable Sensor Data

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1685
Author(s):  
Sakorn Mekruksavanich ◽  
Anuchit Jitpattanakul
Keyword(s):  
Neural Networks ◽  
Activity Recognition ◽  
Human Activities ◽  
Recognition Performance ◽  
Confusion Matrix ◽  
Experimental Studies ◽  
Industrial Applications ◽  
Sensor Data ◽  
Complex Activity ◽  
Activity Data

Sensor-based human activity recognition (S-HAR) has become an important and high-impact topic of research within human-centered computing. In the last decade, successful applications of S-HAR have been presented through fruitful academic research and industrial applications, including for healthcare monitoring, smart home controlling, and daily sport tracking. However, the growing requirements of many current applications for recognizing complex human activities (CHA) have begun to attract the attention of the HAR research field when compared with simple human activities (SHA). S-HAR has shown that deep learning (DL), a type of machine learning based on complicated artificial neural networks, has a significant degree of recognition efficiency. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are two different types of DL methods that have been successfully applied to the S-HAR challenge in recent years. In this paper, we focused on four RNN-based DL models (LSTMs, BiLSTMs, GRUs, and BiGRUs) that performed complex activity recognition tasks. The efficiency of four hybrid DL models that combine convolutional layers with the efficient RNN-based models was also studied. Experimental studies on the UTwente dataset demonstrated that the suggested hybrid RNN-based models achieved a high level of recognition performance along with a variety of performance indicators, including accuracy, F1-score, and confusion matrix. The experimental results show that the hybrid DL model called CNN-BiGRU outperformed the other DL models with a high accuracy of 98.89% when using only complex activity data. Moreover, the CNN-BiGRU model also achieved the highest recognition performance in other scenarios (99.44% by using only simple activity data and 98.78% with a combination of simple and complex activities).

scholarly journals Deep Malaria Parasite Detection in Thin Blood Smear Microscopic Images

2021 ◽  
Vol 11 (5) ◽  
pp. 2284
Author(s):  
Asma Maqsood ◽  
Muhammad Shahid Farid ◽  
Muhammad Hassan Khan ◽  
Marcin Grzegorzek
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Red Blood Cells ◽  
Convolutional Neural Networks ◽  
Blood Cells ◽  
Superior Performance ◽  
Learning Models ◽  
Infected Female ◽  
The World ◽  
Augmentation Techniques

Malaria is a disease activated by a type of microscopic parasite transmitted from infected female mosquito bites to humans. Malaria is a fatal disease that is endemic in many regions of the world. Quick diagnosis of this disease will be very valuable for patients, as traditional methods require tedious work for its detection. Recently, some automated methods have been proposed that exploit hand-crafted feature extraction techniques however, their accuracies are not reliable. Deep learning approaches modernize the world with their superior performance. Convolutional Neural Networks (CNN) are vastly scalable for image classification tasks that extract features through hidden layers of the model without any handcrafting. The detection of malaria-infected red blood cells from segmented microscopic blood images using convolutional neural networks can assist in quick diagnosis, and this will be useful for regions with fewer healthcare experts. The contributions of this paper are two-fold. First, we evaluate the performance of different existing deep learning models for efficient malaria detection. Second, we propose a customized CNN model that outperforms all observed deep learning models. It exploits the bilateral filtering and image augmentation techniques for highlighting features of red blood cells before training the model. Due to image augmentation techniques, the customized CNN model is generalized and avoids over-fitting. All experimental evaluations are performed on the benchmark NIH Malaria Dataset, and the results reveal that the proposed algorithm is 96.82% accurate in detecting malaria from the microscopic blood smears.

scholarly journals A Survey of Encoding Techniques for Signal Processing in Spiking Neural Networks

2021 ◽  
Author(s):  
Daniel Auge ◽  
Julian Hille ◽  
Etienne Mueller ◽  
Alois Knoll
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Industrial Applications ◽  
Spiking Neural Networks ◽  
Biologically Inspired ◽  
Power Efficient ◽  
Signal Encoding ◽  
Uniform Nomenclature ◽  
Theoretical Foundations ◽  
Do So

AbstractBiologically inspired spiking neural networks are increasingly popular in the field of artificial intelligence due to their ability to solve complex problems while being power efficient. They do so by leveraging the timing of discrete spikes as main information carrier. Though, industrial applications are still lacking, partially because the question of how to encode incoming data into discrete spike events cannot be uniformly answered. In this paper, we summarise the signal encoding schemes presented in the literature and propose a uniform nomenclature to prevent the vague usage of ambiguous definitions. Therefore we survey both, the theoretical foundations as well as applications of the encoding schemes. This work provides a foundation in spiking signal encoding and gives an overview over different application-oriented implementations which utilise the schemes.

Learning About the World by Learning About Images

2021 ◽  
pp. 096372142199033
Author(s):  
Katherine R. Storrs ◽  
Roland W. Fleming
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Visual Processing ◽  
Visual Experience ◽  
Natural Images ◽  
Sensory Coding ◽  
The World ◽  
Unsupervised Deep Learning ◽  
Statistical Regularities ◽  
Sensory Encoding

One of the deepest insights in neuroscience is that sensory encoding should take advantage of statistical regularities. Humans’ visual experience contains many redundancies: Scenes mostly stay the same from moment to moment, and nearby image locations usually have similar colors. A visual system that knows which regularities shape natural images can exploit them to encode scenes compactly or guess what will happen next. Although these principles have been appreciated for more than 60 years, until recently it has been possible to convert them into explicit models only for the earliest stages of visual processing. But recent advances in unsupervised deep learning have changed that. Neural networks can be taught to compress images or make predictions in space or time. In the process, they learn the statistical regularities that structure images, which in turn often reflect physical objects and processes in the outside world. The astonishing accomplishments of unsupervised deep learning reaffirm the importance of learning statistical regularities for sensory coding and provide a coherent framework for how knowledge of the outside world gets into visual cortex.

scholarly journals Nanotechnology an Option in Mexican Agriculture

2020 ◽  
pp. 1-3
Author(s):  
Jaime Cuauhtémoc Negrete ◽  
Keyword(s):  
Neural Networks ◽  
Urban Population ◽  
Decision Makers ◽  
Artificial Vision ◽  
Healthy Development ◽  
The World ◽  
Food Dependency ◽  
And Migration ◽  
Day By Day ◽  
Modern Technologies

In Mexico, agriculture is increasingly less profitable, due to this situation its contribution to the country’s economy decreases day by day, which has encouraged the import of food, thus causing poverty and migration, food dependency and other undesirable situations for the healthy development of society in general, not only the rural population but also the urban population is affected, so all the technologies that can change this scenario are sought and explored, one of them being Nanotechnology, despite the benefits of nanotechnology, as is always the case, it is not applied in agriculture as most of the most widely used technologies in the world today, such as mechatronics, and other modern technologies such as neural networks, Artificial vision, and internet of things , this is also happening with nanotechnology, De la Revision made only some Authors have done the rest of the research only publishing revisions of research done abroad, so it is proposed that decision makers support researchers who really do work on this discipline that is so promising at present for the good of our agriculture , this being the objective of this work will review what has been done in this field in this regard in the nation

scholarly journals INDUSTRIAL APPLICATIONS OF ARTIFICIAL NEURAL NETWORKS

2014 ◽  
pp. 8-20
Author(s):  
Kurosh Madani
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real World ◽  
Industrial Applications ◽  
Large Field ◽  
Nonlinear Functions ◽  
The Past ◽  
Real World Applications ◽  
Ann Models ◽  
Artificial Neural

In a large number of real world dilemmas and related applications the modeling of complex behavior is the central point. Over the past decades, new approaches based on Artificial Neural Networks (ANN) have been proposed to solve problems related to optimization, modeling, decision making, classification, data mining or nonlinear functions (behavior) approximation. Inspired from biological nervous systems and brain structure, Artificial Neural Networks could be seen as information processing systems, which allow elaboration of many original techniques covering a large field of applications. Among their most appealing properties, one can quote their learning and generalization capabilities. The main goal of this paper is to present, through some of main ANN models and based techniques, their real application capability in real world industrial dilemmas. Several examples through industrial and real world applications have been presented and discussed.

scholarly journals Preface

2021 ◽  
Vol 2050 (1) ◽  
pp. 011001
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Industrial Applications ◽  
Program Committee ◽  
Technical Session ◽  
Technical Program ◽  
Content Type ◽  
The World ◽  
Travel Restrictions ◽  
Virtual Conference

Considering the current situation of COVID-19 and travel restrictions, the 3rd International Conference on Industrial Applications of Big Data and Artificial Intelligence (BDAI 2021) which was planned to be held in Wuhan. China from Sept. 23 to 25, 2021 was changed into virtual conference on Sept. 24, 2021 via Tencent Meeting (Voov) software. BDAI 2021 was organized by China University of Geosciences (Wuhan), sponsored by Hong Kong Society of Mechanical Engineers (HKSME). The Technical Program committee received a total of 38 paper submissions from all over the world, among which 20 papers were accepted, and more than 30 participants attended the conference online, they were from China, Australia, Thailand, Malaysia, India, Japan, UK and more. Four renowned speakers given speeches about their latest research and reports. They are: Prof. Dan Zhang from York University, Canada; Prof. Lefei Zhang from Wuhan University. China: Prof. Deze Zeng from China University of Geosciences (Wuhan), China and Assoc. Prof. Simon James Fong from University of Macau. Macau S.A.R., China. The conference also had 1 technical session and 1 poster sessions. This conference aims to provide a platform for researchers and engineers to share their ideas, recent developments, and successful practices in energy engineering. The participants of the conference were from almost every part of the world, with various background such as academia, industry, and well-known entrepreneurs. Each keynote speech lasted 40 minutes, and authors presentation 15 minutes. Each presentation was included with questions and answers. BDAI 2021 became an effective communication platform for all the participants over the world and unlike some that claim international reach this conference was truly international. The conference proceeding is a compilation of the accepted papers and represent an interesting outcome of the conference. This book covers 3 chapters: 1. Artificial Intelligence: 2. Big Data Technology; 3. Robot System. We would like to acknowledge all of those who supported BDAI 2021. Each individual and institutional help were very important for the success of this conference. Especially we would like to thank the committee chairs, committee members and reviewers, for their tremendous contribution in conference organization and peer review of the papers. We sincerely hope that BDAI 2021 will be a fomrn for excellent discussions that will put forward new ideas and promote collaborative research and support researchers as they take their work forward. We are sure that the proceedings will serve as an important research source of references and the knowledge, which will lead to not only scientific and engineering progress but also other new products and processes. Dan Zhang, York University, Canada

scholarly journals In the footsteps of Einstein and Wiener

2021 ◽  
Author(s):  
Emir Ashursky
Keyword(s):  
Neural Networks ◽  
A Priori ◽  
Electron Neutrino ◽  
Self Organization ◽  
Mathematical Foundation ◽  
Molecular Models ◽  
Computer Clusters ◽  
Modern Computer ◽  
The World ◽  
The Universe

To date the recognition of universal, a priori inherent in them connection between the objects of the world around us is quite rightly considered almost an accomplished fact. But on what laws do these or those sometimes rather variegated systems function in live and inert nature (including - in modern computer clusters)? Where are the origins of their self-organization activity lurked: whether at the level of still hypothetical quantum-molecular models, finite bio-automata or hugely fashionable now artificial neural networks? Answers to all these questions if perhaps will ever appear then certainly not soon. That is why the bold innovative developments presented in following article are capable in something, possibly, even to refresh the database of informatics so familiar to many of us. And moreover, in principle, the pivotal idea developed here, frankly speaking, is quite simple in itself: if, for example, the laws of the universe are one, then all the characteristic differences between any evolving objects should be determined by their outwardly-hidden informative (or, according to author’s terminology - “mental") rationale. By the way, these are not at all empty words, as it might seem at first glance, because they are fully, where possible, supported with the generally accepted physical & mathematical foundation here. So as a result, the reader by himself comes sooner or later to the inevitable conclusion, to wit: only the smallest electron-neutrino ensembles contain everything the most valuable and meaningful for any natural system! At that even no matter, what namely global outlook paradigm we here hold...

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