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Artificial Intelligence-Based Recommendation System for Detecting and Diagnosing Broken Bars in Induction Motors Under Transient Operation

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IFAC-PapersOnLine 2024年第19期58卷 1156-1161页

作者： Narco A.R. Maciejewski Roberto Z. Freire Anderson L. Szejka Thiago P.M. Bazzo Sofia M.A. Lopes Rogério A. Flauzino Department of Electrical Engineering - DAELT-CT Universidade Tecnológica Federal do Paraná - UTFPR Curitiba PR 80230-90 Brazil Production and Systems Engineering - PPGEPS-PB Energy Systems - PPGSE-CT Industrial and Systems Engineering Graduate Program - PPGEPS Pontifical Catholic University of Parana - PUCPR Curitiba PR 80215-901 Brazil Electrical Engineering - PPGEE-SC São Carlos School of Engineering - EESC Universidade de São Paulo - USP São Carlos SP 13566-590 Brazil

Three-phase induction motors are the main elements for converting electrical energy into mechanical energy and are extensively used in industry. Reducing maintenance costs becomes an incentive for developing systems capable of identifying defects. This research proposes a framework for recommending machine learning algorithms that diagnose and detect broken bar defects in three-phase induction motors under transient operation based on artificial intelligence. Employing experimental data, features were extracted and selected based on current, voltage, and vibration. A protocol of insertion of white noise showed that the proposed framework admitted 80% of noise without losing the predictive capacity based on a multicriteria performance measure.

关键词： condition monitoring fault detection fault classification machine learning maintenance engineering smart systems three-phase induction motor

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AHP Decision-Making Approach for Solar Photovoltaic (PV) Modules End-Of-Life (EOL) Disposition Strategies

AHP Decision-Making Approach for Solar Photovoltaic (PV) Mod...

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Solar Conference (MENA-SC), Middle East and North Africa

作者： WADHAH ALZAHMI MALICK NDIAYE American University of Sharjah Sharjah Sharjah United Arab Emirates Department of Industrial Engineering Engineering Systems Management Program American University of Sharjah

Increasing energy demands globally have accelerated the development of renewable technologies including solar photovoltaics (PV). However, this development has unavoidably led to the emergence of significant volumes PV waste considering 20–30 years life span before End of Life (EOL). The standard decision makers practices for PV EOL are disposal to the landfill, recycling, or reuse. Due to complexity of the decision environment, this study aims to analyze and rank solar PV disposition alternative using Analytic Hierarchy Process (AHP) based on five main criteria including environmental, economic, social, policy and legislation and technical. AHP Aggregating Individual Priorities (AIP) aggregation method was utilized to evaluate the overall perspective of all the study participant. In addition, individual stakeholder group decision was assessed individually to understand potential different EOL disposition perspective from different stakeholder group. The findings of the analysis shows that the overall ranking of the alternatives based on the main criteria is leaning toward recycling compared to other alternatives. However, there is a variation in the decision between individual stakeholder group.

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Optimal Synergic Deep Learning for COVID-19 Classification Using Chest X-Ray Images

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Computers, Materials & Continua 2023年第6期75卷 5255-5270页

作者： JoséEscorcia-Gutierrez Margarita Gamarra Roosvel Soto-Diaz Safa Alsafari Ayman Yafoz Romany F.Mansour Departament of Computational Science and Electronic Universidad de la CostaCUCBarranquilla080002Colombia Biomedical Engineering Program Universidad Simón BolívarBarranquilla080002Colombia Department of Computer Science and AI Faculty of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the *** X-ray(CXR)gained much interest after the COVID-19 outbreak thanks to its rapid imaging time,widespread availability,low cost,and *** radiological investigations,computer-aided diagnostic tools are implemented to reduce intra-and inter-observer *** lately industrialized Artificial Intelligence(AI)algorithms and radiological techniques to diagnose and classify disease is *** current study develops an automatic identification and classification model for CXR pictures using Gaussian Fil-tering based Optimized Synergic Deep Learning using Remora Optimization Algorithm(GF-OSDL-ROA).This method is inclusive of preprocessing and classification based on *** data is preprocessed using Gaussian filtering(GF)to remove any extraneous noise from the image’s ***,the OSDL model is applied to classify the CXRs under different severity levels based on CXR *** learning rate of OSDL is optimized with the help of ROA for COVID-19 diagnosis showing the novelty of the *** model,applied in this study,was validated using the COVID-19 *** experiments were conducted upon the proposed OSDL model,which achieved a classification accuracy of 99.83%,while the current Convolutional Neural Network achieved less classification accuracy,i.e.,98.14%.

关键词： Artificial intelligence chest X-ray COVID-19 optimized synergic deep learning preprocessing public health

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Recommending View Bundles in Data Marketplaces

Recommending View Bundles in Data Marketplaces

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2022 IEEE International Conference on systems, Man, and Cybernetics, SMC 2022

作者： De Carvalho, Thiego Buenos Aires Martins, Denis M. Lima De Lima Neto, Fernando B. Vossen, Gottfried University of Pernambuco Computing Engineering Program Recife Brazil University of Münster Ercis Machine Learning and Data Engineering Münster Germany University of Münster Ercis Databases and Information Systems Münster Germany

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

For companies to have a competitive advantage, they need to extract relevant information from data and for that, they need to complement their own data with other data sources. Data marketplaces are platforms on which data providers and data consumers do business. However, every data interaction incurs a monetary cost. Therefore, data consumers are interested in buying a set of interesting views that fit their (goal and) budget. Views allow data to be represented visually, enabling users to make sense of patterns and insights. Besides allowing for easier cost control, buying a set of views bundled together increases the chance of finding what consumers want over buying them view-by-view. Selecting the suitable views to compose an interesting bundle is non-trivial, due to the vast number of view combinations that potentially meet the data consumer's needs. In this paper, we address the problem of view bundle recommendation in data marketplaces, in which the utility of a bundle depends on the interplay among candidate views. We propose the use of Self-Organizing Maps as a means to compute this interplay and use a Genetic Algorithm to design near-optimal bundles. Our empirical results demonstrate that our approach can effectively aid data consumers to find relevant view bundles under budget constraints. © 2022 IEEE.

关键词： Genetic algorithms

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Fuzzy C-means Cluster Pattern Analysis and Ward Model Mapping in Viewing the Growth of Infectious and Non-Infectious Diseases Children in North Aceh

Fuzzy C-means Cluster Pattern Analysis and Ward Model Mappin...

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Artificial Intelligence and Image Processing (IWAIIP), International Workshop on

作者： Mauliza Mauliza Mutammimul Ula Ilham Sahputra Rosya Afdelina Medical Education Study Program Faculty of Medicine Universitas Malikussaleh Lhokseumawe Indonesia Information Systems Study Program Faculty of Engineering Universitas Malikussaleh Lhokseumawe Indonesia

The problems and challenges for the government in seeing disease growth trend patterns are very important. This research focuses on infectious diseases, namely Dengue Hemorrhagic Fever (DHF), while non-communicable illnesses focus on epilepsy and thalassemia in 2023-2024. The priority in this research is that the Health Service can take action to prevent disease distribution patterns that are seen based on the results of identifying patterns and trends using the fuzzy c-means model and mapping for each region. The research methodology includes collecting patient data, inputted by recorded medical data consisting of sub-district data and the number of incidents. The research results of the Fuzzy C-means model in analyzing infectious disease trend patterns show 3 clusters. The first cluster of vulnerable areas has four sub-districts: Sawang, Syamtalira Bayu, Dewantara and Muara Batu. Then, the second cluster still consists of 18 sub-districts. Finally, the safe cluster consists of 8 sub-districts. Meanwhile, the results of the ward model research showed that there were 2 clusters, namely vulnerable consisting of 19 sub-districts and safe eight sub-districts, which were then included in the spatial map. Meanwhile, analysis of non-communicable disease patterns using fuzzy c-means, namely thalassemia and epilepsy in cluster C1 (High) in 4 and 7 sub-districts, medium cluster in 9 and 7, and low cluster in 14 and 16 sub-districts. Therefore, this research can provide an important picture for the Health Service in analyzing trends in cluster patterns of growth of infectious and non-communicable diseases in children.

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Towards an integrated MCDM and BSC method to support the digital transformation strategy in railway companies 28

Towards an integrated MCDM and BSC method to support the dig...

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28th ISTE International Conference on Transdisciplinary engineering, TE 2021

作者： Zanon, Gabriel Nogueira Szejka, Anderson Luis De Freitas Rocha Loures, Eduardo Industrial and Systems Engineering Graduate Program - PPGEPS Pontifical Catholic University of Paraná - PUCPR Curitiba Brazil

ISBN: (纸本)9781643682082

Digital transformation is now widely discussed and applied in different enterprises and under various organizational aspects. Traditional industries recognize the need to innovate and digitalize their business processes and models. Among these industries, the railway sector presents a vast space for digital transformation. The railway has several operational, regulatory, regional and technological specificities that must be taken into account in the digitalization processes. Given the multiple aspects involved and the transdisciplinarity, the digital transformation strategy and roadmap of companies must be defined in a way that fits it to the corporate strategy, allowing digital transformation program to be implemented effectively, prioritizing internal sectors with the greatest strategic impact, generating greater productivity, business models improvement, security and, above all, competitiveness for the sector. This research aims to propose a model for the definition of the digital transformation strategy in the railway companies using the Balanced Scorecard (BSC) as a tool for strategic organizational analysis and multi-criteria decision-making (MCDM) to define the drivers for digital transformation in the railway. Finally, to demonstrate the proposal, a case study will be carried out at a Brazilian railway company. © 2021 The authors and IOS Press.

关键词： Decision making

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Optimized Silicon Nitride-Based Passive Components for 850 nm OCT on Photonic Chips

Optimized Silicon Nitride-Based Passive Components for 850 n...

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2024 Laser Science, LS 2024

作者： Meng, Jiawei Hao, Senyue Adkins, Aaron J. Zhou, Weiyan Zhou, Chao Department of Biomedical Engineering Washington University in St. Louis St. LouisMO63130 United States Department of Electrical & Systems Engineering Washington University in St. Louis St. LouisMO63130 United States Imaging Science PhD Program Washington University in St. Louis St. LouisMO63130 United States

For optical coherence tomography (OCT) on photonic chips, we designed and validated optimized silicon nitride-based passive components for photonic integrated circuits. Centered at 850 nm, these components offer high ... 详细信息

关键词： Silicon nitride

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Towards a smart reconfiguration process for complex product manufacturing based on industrial robot cells

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IFAC-PapersOnLine 2022年第2期55卷 108-113页

作者： Murillo Skrzek Leandro L. da Silva Anderson L. Szejka Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Parana (PUCPR) Curitiba Brazil

The cost for companies that can custom their production line with precision and quality, through machining centres and laser cutting machines, is expensive, as the degree of machining freedom increases. Furthermore, the greater the complexity of the product to be manufactured, the more processes and equipment are needed to manufacture it. In this context, this research proposes a smart reconfiguration process for complex product manufacturing, based on industrial robotic manipulators, with 6 or more degrees of freedom, superior to conventional methods. With the integration of SolidWorks and PowerMill software, it was possible to apply the solution in an experimental case of blisk manufacturing milling and laser cutting of an engine head gasket. With CAD, CAM and robot simulation by the PowerMill tool, it was possible to validate the proposed solution, obtaining process data and information of milling and laser cutting. With the analysis of the simulation results, it was possible to identify the limitations of the project, such as (i) restriction in the precision of the machining of the product; (ii) amount of real-time supervision and monitoring information; and (iii) work area limit. However, the project can integrate two manufacturing processes to machine and cut complex parts with flexibility.

关键词： Robotic manipulator Milling Laser Cutting Reconfigurable Manufacturing systems Complex Product Computer-Aided Manufacturing

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Training Swinging Door Trending Compression Algorithm for IoT devices

Training Swinging Door Trending Compression Algorithm for Io...

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IEEE World Forum on Internet of Things (WF-IoT)

作者： Juan David Arias Correa Alex Sandro Roschildt Pinto Carlos Montez Post-graduate Program of Automation Systems Engineering Federal University of Santa Catarina (UFSC) Florianópolis Brazil Dept. of Informatics and Statistic Dept. of Automation and Systems Engineering Federal University of Santa Catarina (UFSC) Florianópolis Brazil

ISBN: (数字)9798350373011

ISBN: (纸本)9798350373028

The communication system is essential for the devices used in IoT. Nevertheless, the consumption caused by transmitting and receiving data is a concern in many scenarios. That is due to these devices commonly having limited energy and computational capacities. Transmission of redundant or irrelevant samples frequently wastes the device’s resources. Furthermore, Storing a large amount of redundant data could consume storage space and not offer any benefit. Data Compression (DC) methods are a potential solution. DC could reduce communication usage and storage demand. This research proposes the Training Swing Door Trending (TSDT) for being implemented in IoT Devices. TSDT is a new algorithm that improves the classic Swing Door Trending (STD). They represent the data by trend lines and have a constant computational complexity. TSDT has a training step for the automatic configuration of its parameters. This article additionally presents the Compression Factor (C-Score), a new quality metric to analyze the compression results in lossy DC methods. C-Score takes as a basis the F-Score, a measure of predictive performance. The C-Score uses the Compression and Error metrics to evaluate the compression performance in Lossy Algorithms.

关键词： Training Performance evaluation Communication systems Data compression Prediction algorithms Market research Internet of Things Computational complexity Compression algorithms

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Smart greenhouses as the path towards precision agriculture in the food-energy and water nexus: case study of Qatar

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Environment systems and Decisions 2022年第4期42卷 521-546页

作者： Karanisa, Theodora Achour, Yasmine Ouammi, Ahmed Sayadi, Sami Biotechnology Program Center for Sustainable Development College of Arts and Sciences Qatar University Doha 2713 Qatar Electrical Systems and Telecommunications Engineering Laboratory The National School of Applied Sciences of Kenitra 14000 Kenitra Morocco

Greenhouse farming is essential in increasing domestic crop production in countries with limited resources and a harsh climate like Qatar. Smart greenhouse development is even more important to overcome these limitations and achieve high levels of food security. While the main aim of greenhouses is to offer an appropriate environment for high-yield production while protecting crops from adverse climate conditions, smart greenhouses provide precise regulation and control of the microclimate variables by utilizing the latest control techniques, advanced metering and communication infrastructures, and smart management systems thus providing the optimal environment for crop development. However, due to the development of information technology, greenhouses are undergoing a big transformation. In fact, the new generation of greenhouses has gone from simple constructions to sophisticated factories that drive agricultural production at the minimum possible cost. The main objective of this paper is to present a comprehensive understanding framework of the actual greenhouse development in Qatar, so as to be able to support the transition to sustainable precision agriculture. Qatar’s greenhouse market is a dynamic sector, and it is expected to mark double-digit growth by 2025. Thus, this study may offer effective supporting information to decision and policy makers, professionals, and end-users in introducing new technologies and taking advantage of monitoring techniques, artificial intelligence, and communication infrastructure in the agriculture sector by adopting smart greenhouses, consequently enhancing the Food-Energy-Water Nexus resilience and sustainable development. Furthermore, an analysis of the actual agriculture situation in Qatar is provided by examining its potential development regarding the existing drivers and barriers. Finally, the study presents the policy measures already implemented in Qatar and analyses the future development of the local greenhouse sect

关键词： controlled environment agriculture (CEA) FEW nexus Food security Greenhouse technology Precision agriculture Sustainable development

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