scholarly journals Is Artificial Intelligence (A.I.) Ready to Run a Factory?

2021 ◽  
pp. 126-132
Author(s):  
Craig Eric Seidelson
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Nonlinear Systems ◽  
Linear Systems ◽  
Chaotic Systems ◽  
Smart Manufacturing ◽  
Total Investment ◽  
Input Variables ◽  
On Time Delivery ◽  
A Company

With smart factory investment expected to increase 20% year-on-year over the next five years and total investment expected to reach $275 billion worldwide by 2027, the use of Artificial Intelligence (A.I.) to manage operations is receiving considerable attention.  This paper takes an in depth look at how factory data is being generated, stored, processed, transferred, trained and ultimately validated using A.I.  The conclusion is that deep machine learning is more than capable of controlling devices.  Yet, research shows only 14% of smart manufactures would describe their A.I. efforts as successful.  The problems are cost and application.  Smart manufacturing is almost exclusively done by multi-billion dollar operations.  Is this money well spent?  Factories aren’t closed, linear systems. In these chaotic systems infinitesimal changes in any one of the myriad of input variables are capable of producing disproportionate changes in output values. As a result, no matter how much scrap, downtime, sales or on-time delivery data a company collects actual values will diverge exponentially from what existing A.I. algorithms are predicting.  Until more research is done predicting dynamic, nonlinear systems A.I. will not be capable of running a factory without human involvement.

scholarly journals Artificial intelligence method to classify ophthalmic emergency severity based on symptoms: a validation study

BMJ Open ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. e037161
Author(s):  
Hyunmin Ahn
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Model Performance ◽  
Armed Forces ◽  
Validation Data ◽  
Data Set ◽  
Orange Yellow ◽  
Hospital Visits ◽  
Input Variables ◽  
Fully Connected

ObjectivesWe investigated the usefulness of machine learning artificial intelligence (AI) in classifying the severity of ophthalmic emergency for timely hospital visits.Study designThis retrospective study analysed the patients who first visited the Armed Forces Daegu Hospital between May and December 2019. General patient information, events and symptoms were input variables. Events, symptoms, diagnoses and treatments were output variables. The output variables were classified into four classes (red, orange, yellow and green, indicating immediate to no emergency cases). About 200 cases of the class-balanced validation data set were randomly selected before all training procedures. An ensemble AI model using combinations of fully connected neural networks with the synthetic minority oversampling technique algorithm was adopted.ParticipantsA total of 1681 patients were included.Major outcomesModel performance was evaluated using accuracy, precision, recall and F1 scores.ResultsThe accuracy of the model was 99.05%. The precision of each class (red, orange, yellow and green) was 100%, 98.10%, 92.73% and 100%. The recalls of each class were 100%, 100%, 98.08% and 95.33%. The F1 scores of each class were 100%, 99.04%, 95.33% and 96.00%.ConclusionsWe provided support for an AI method to classify ophthalmic emergency severity based on symptoms.

scholarly journals New Business and Operating Models. Optimization of a Blast Furnace in the Steel Industry. Machine Learning as a Process Optimization

2021 ◽  
Author(s):  
Andrés Redchuk ◽  
Federico Walas Mateo
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Blast Furnace ◽  
Conceptual Framework ◽  
Manufacturing Process ◽  
Smart Manufacturing ◽  
Key Factors ◽  
Steel Company ◽  
Steel Manufacturing ◽  
New Business

The article takes the case of the adoption of machine learning in a steel manufacturing process through a platform provided by a novel Canadian startup, Canvass Analytics. This way the steel company could optimize the process in a blast furnace. The content of the paper includes a conceptual framework on key factors around steel manufacturing and machine learning. Method: The article takes the case of the adoption of machine learning in a steel manufacturing process through a platform provided by a novel Canadian startup, Canvass Analytics. This way the steel company could optimize the process in a blast furnace. The content of the paper includes a conceptual framework on key factors around steel manufacturing and machine learning. Results: This case is relevant for the authors by the way the business model proposed by the startup attempts to democratize Artificial Intelligence and Machine Learning in industrial environments. This way the startup delivers value to facilitate traditional industries to obtain better operational results, and contribute to a better use of resources. Conclusion: This work is focused on opportunities that arise around Artificial Intelligence as a driver for new business and operating models. Besides the paper looks into the framework of the adoption of Artificial Intelligence and Machine Learning in a traditional industrial environment towards a smart manufacturing approach.

scholarly journals POSSIBILITIES OF APPLICATION MACHINE LEARNING METHODS IN SOLVING ANALYTICAL PROBLEMS IN METALLURGICAL INDUSTRY

2021 ◽  
Vol 9 (3) ◽  
pp. 61-65
Author(s):  
Diana Yusupova ◽  
Sergey Muzalev
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Metallurgical Industry ◽  
Learning Opportunities ◽  
High Tech ◽  
Learning Methods ◽  
Advantages And Disadvantages ◽  
Machine Learning Methods ◽  
Non Linear ◽  
A Company

Background. Machine learning is a promising field for organization in the age of development of high-tech methods of management and organization of the company. As a rule, this term is used in relation to artificial intelligence, namely, machines that could learn independently. Thus, the main goal of this work is to assess the prospects for using these methods for solving various problems in a corporation. Methods. The article introduces the main methods of machine learning, their analysis, linear and non-linear learning methods are given, their use in practice is indicated, and the key advantages of using a trained artificial intelligence in a company are identified. Result. As a result, the author proposes ways of using machine learning methods in a firm, analyzes their advantages and disadvantages, identifies the problems of implementing artificial intelligence learning opportunities in practice.

scholarly journals Automatic electronic invoice classification using machine learning models

2020 ◽  
Author(s):  
Chiara Bardelli ◽  
Alessandro Rondinelli ◽  
Ruggero Vecchio ◽  
Silvia Figini
Keyword(s):  
Machine Learning ◽  
Prediction Accuracy ◽  
Intelligent System ◽  
Classification Problem ◽  
The Hierarchical Structure ◽  
Input Variables ◽  
Economic Nature ◽  
Financial Transactions ◽  
A Company ◽  
Machine Learning Models

Electronic invoicing has become mandatory for Italian companies since January 2019. Invoices are structured in a predefined xml template where the information reported can be easily extracted and analyzed. The main aim of this paper is to exploit the information structured in electronic invoices to build an intelligent system which can facilitate accountants work. More precisely, this contribution shows how it is possible to automate part of the accounting process: all sent or received invoices of a company are classified into specific codes which represent the economic nature of the the financial transactions. In order to classify data contained in the invoices a machine learning multiclass classification problem is proposed using as input variables the information of the invoices to predict two different target variables, account codes and the VAT codes, which composes a general ledger entry. Different approaches are compared in terms of prediction accuracy. The best performance is achieved considering the hierarchical structure of the account codes.

scholarly journals A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests

2017 ◽  
Vol 139 (7) ◽  
Author(s):  
Dazhong Wu ◽  
Connor Jennings ◽  
Janis Terpenny ◽  
Robert X. Gao ◽  
Soundar Kumara
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Tool Wear ◽  
Random Forests ◽  
Manufacturing Systems ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Smart Manufacturing ◽  
Tool Wear Prediction

Manufacturers have faced an increasing need for the development of predictive models that predict mechanical failures and the remaining useful life (RUL) of manufacturing systems or components. Classical model-based or physics-based prognostics often require an in-depth physical understanding of the system of interest to develop closed-form mathematical models. However, prior knowledge of system behavior is not always available, especially for complex manufacturing systems and processes. To complement model-based prognostics, data-driven methods have been increasingly applied to machinery prognostics and maintenance management, transforming legacy manufacturing systems into smart manufacturing systems with artificial intelligence. While previous research has demonstrated the effectiveness of data-driven methods, most of these prognostic methods are based on classical machine learning techniques, such as artificial neural networks (ANNs) and support vector regression (SVR). With the rapid advancement in artificial intelligence, various machine learning algorithms have been developed and widely applied in many engineering fields. The objective of this research is to introduce a random forests (RFs)-based prognostic method for tool wear prediction as well as compare the performance of RFs with feed-forward back propagation (FFBP) ANNs and SVR. Specifically, the performance of FFBP ANNs, SVR, and RFs are compared using an experimental data collected from 315 milling tests. Experimental results have shown that RFs can generate more accurate predictions than FFBP ANNs with a single hidden layer and SVR.

scholarly journals IIoT/IoT and Artificial Intelligence/Machine Learning as a Process Optimization driver under industry 4.0 model

2021 ◽  
Vol 21 (2) ◽  
pp. e15
Author(s):  
Federico Walas ◽  
Andrés Redchuk
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Process Optimization ◽  
Industry 4.0 ◽  
Industrial Process ◽  
Value Added ◽  
Smart Manufacturing ◽  
Industrial Plants ◽  
A Value ◽  
Learning As A Process

The advance of digitalization in industry is making possible that connected products and processes help people, industrial plants and equipment to be more productive and efficient, and the results for operative processes should impact throughout the economy and the environment.Connected products and processes generate data that is being seen as a key source of competitive advantage, and the management and processing of that data is generating new challenges in the industrial environment.The article to be presented looks into the framework of the adoption of Artificial Intelligence and Machine Learning and its integration with IIoT or IoT under industry 4.0, or smart manufacturing framework. This work is focused on the discussion around Artificial Intelligence/Machine Learning and IIoT/IoT as driver for Industrial Process optimization.The paper explore some related articles that were find relevant to start the discussion, and includes a bibliometric analysis of the key topics around Artificial Intelligence/Machine Learning as a value added solution for process optimization under Industry 4.0 or Smart Manufacturing paradigm.The main findings are related to the importance that the subject has acquired since 2013 in terms of published articles, and the complexity of the approach of the issue proposed by this work in the industrial environment.

scholarly journals Tomographic image correction with noise reduction algorithms

2019 ◽  
Vol 252 ◽  
pp. 09001
Author(s):  
Grzegorz Kłosowski ◽  
Tomasz Rymarczyk ◽  
Edward Kozłowski
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Input Data ◽  
Tomographic Image ◽  
Image Correction ◽  
Simulation Experiments ◽  
Tomographic Images ◽  
Input Variables ◽  
Original Approach

This article presents an original approach to improve the results of tomographic reconstructions by denoising the input data, which affects output images improving. The algorithms used in the research are based on autoencoders and Elastic Net - both related to artificial intelligence or machine-learning developed controllers. Due to the reduction of unnecessary features and removal of mutually correlated input variables generated by the tomography electrodes, good quality reconstructions of tomographic images were obtained. The simulation experiments proved that the presented methods could be effective in improving the quality of reconstructed tomographic images.

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

Development of innovative hygienic technologies for preservation of the workers’ reproductive health

2020 ◽  
pp. 792-792
Author(s):  
M. A. Fesenko ◽  
G. V. Golovaneva ◽  
A. V. Miskevich
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Reproductive Health ◽  
Reproductive System ◽  
Preventive Measures ◽  
Learning Algorithms ◽  
Reproductive Function ◽  
Machine Learning Algorithms ◽  
New Model

The new model «Prognosis of men’ reproductive function disorders» was developed. The machine learning algorithms (artificial intelligence) was used for this purpose, the model has high prognosis accuracy. The aim of the model applying is prioritize diagnostic and preventive measures to minimize reproductive system diseases complications and preserve workers’ health and efficiency.

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