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Annual Conference of Industrial Electronics Society

作者： Mukund Bhole Wolfgang Kastner Thilo Sauter Institute of Computer Engineering TU Wien Vienna Austria Dep. of Integrated Sensor Systems Institute of Computer Technology TU Wien Danube University Krems Austria

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

In response to the growing need for compliance with diverse safety and security regulations, crucial for safeguarding both humans and machines in the era of Industry 4.0 (I4.0), it has also become increasingly crucial for industries to ensure adherence to safety and security standards. However, navigating the complexities of regulations and standards can be daunting, often leading to inefficiencies in compliance processes. Our goal is to foster a comprehensive understanding of an effective approach to meeting compliance requirements in these related fields. Therefore, this paper introduces a semi-automated method for ensuring compliance with unified safety and security standards within the context of I4.0. This approach empowers management teams to thoroughly analyze the specific requirements necessary to maintain a protected environment. Compliance can be achieved at both the asset component and system levels, aligning with the relevant standards. Additionally, we present a detailed analysis of a use case and its output, demonstrating the practical application and efficacy of our approach.

关键词： Industries Accuracy Standards organizations Manuals Regulation Safety Fourth Industrial Revolution Complexity theory Security Certification

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Performance Analysis of Intelligent Reflecting Surfaces for 5G/6G-Enabled Future Smart Industries with a Focus on Millimeter-Wave Band Communications

Performance Analysis of Intelligent Reflecting Surfaces for ...

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IEEE International Conference on Communications Workshops, ICC

作者： Muhammad Farhan Khan Ali Raza Adeel Iqbal Adnan Rashid Muhammad Ali Jamshed Dirk Pesch School of Computer Science and Information Technology University College Cork Cork Ireland Dep. of Electrical and Computer Engineering COMSATS University Islamabad Pakistan Dep. of Info. & Com. Engineering Yeungnam University Gyeongsan-si Republic of Korea Dep. of Electrical & Information Engineering Politecnico di Bari Bari Italy James Watt School of Engineering University of Glasgow Glasgow U.K

ISBN: (数字)9798350304053

ISBN: (纸本)9798350304060

Industry 4.0's emergence necessitates reliable communication systems. Millimeter-wave (mmWave) communication is a promising solution, but challenges like limited coverage and atmospheric absorption persist. The Intelligent Reflecting Surfaces (IRS) offers transformative potential by dynamically manipulating wave propagation to improve millimeter-wave (mmWave) communication. This research investigates the impact of the distance of Industrial User Equipment (iUE) from the IRS and Base Station (BS), and the height of the IRS from the ground, along with the impact of sum throughput by increasing the number of IRS elements. The IRS-assisted system exhibits enhanced performance within the mmWave frequency band compared to the sub-6 GHz band. The observed achievable rate gains, exceeding 200% on average, underscore the significant performance enhancement enabled by IRS dep.oyments. Moreover, increasing the number of IRS elements significantly enhances the overall throughput. The decrease in required transmit power with an increasing number of Intelligent Reflecting Surfaces IRS elements can be attributed to the enhanced ability of IRS to efficiently reflect and manipulate electromagnetic waves. As more IRS elements are dep.oyed, they collectively contribute to optimizing signal propagation, allowing for better control and redirection of the transmitted signals. This phenomenon enables the system to achieve the desired communication objectives with less overall transmit power. Additionally, the attainable capacity of the examined IRS-assisted wireless communication system exhibits growth alongside an increase in the elevation of the IRS installation. Results showcase the potential of IRSs to enhance coverage and communication reliability, providing valuable insights for designing efficient wireless systems in smart factories and Industry 4.0 applications.

关键词： Wireless communication Industries Surface waves Conferences Throughput Millimeter wave propagation Reliability engineering

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IT Security Solutions for IT/OT Integration: Identifying Gaps and Opportunities

IT Security Solutions for IT/OT Integration: Identifying Gap...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

In the contemporary landscape of convergence between Information technology (IT) and Operational technology (OT), maintaining asset visibility within the Industrial Control Systems (ICS) infrastructure is paramount. Yet, the escalating targeting of legacy systems and air-gapped networks by cyber threats underscores the need for robust security measures and solutions to counter unauthorized access and malicious activities. This paper presents an analysis aimed at identifying gaps and opportunities in integrating IT Security Solutions with OT environments. These solutions offer comprehensive features such as real-time threat detection, asset identification, network monitoring, and incident response capabilities tailored specifically for the unique challenges posed by industrial control networks. Leveraging the Festo MPS 403–1 system as a case study, we delve into the intricacies of IT/ OT integration and highlight areas necessitating improvement to achieve seamless convergence and harness the advantages of unified systems across industries.

关键词： Industries Industrial control Security management Logic gates Software Threat assessment Security Monitoring Industrial Internet of Things Convergence

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Logistic Regression Classification for Assessing the Risk of Kidney Tumor

Logistic Regression Classification for Assessing the Risk of...

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Electrical, Energy, and Artificial Intelligence (EICEEAI), International engineering Conference on

作者： Dalia Alzu'bi Rabia Emhamed Al Mamlook Ahmad Nasayreh Mohammad Aljaidi Rami AlAzab Hasan Gharaibeh Qais Al-Na'amneh Dep. Information and Systems Engineering Concordia University Montreal Canada Dep. of Business Administration Trine University Indiana USA Department of Industrial Engineering University Zawia Tripoli Libia Dep. Computer Science Yarmouk University Irbid Jordan Dep. of Computer Science Zarqa University Zarqa Jordan Dep. Surgery and Urology Jordan University of Science and Technology Irbid Jordan Dep. of Cyber Security Applied Science Private University Amman Jordan

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

The kidneys have the important function of filtering waste products and toxins from the blood in the human body. Any problems affecting the kidneys can potentially impact their efficiency and overall function. Kidney Tumor (KT) is a disease that specifically affects kidney cells and causes the abnormal growth of tissue in one or both kidneys. Assessing the risk of developing a kidney tumor and identifying the key factors that contribute to this risk are crucial for ensuring patient well-being. Additionally, this information assists doctors and specialists in making faster and more accurate diagnoses, determining appropriate treatment methods, and potentially influencing the course of the disease to minimize its severity and impact. Machine learning algorithms have recently been introduced to evaluate disease risks, and in this study, we specifically focus on examining the risk of kidney tumor development and investigating the influencing factors. We employed a logistic regression model to predict if a patient is at risk of developing a kidney tumor or not, and further categorized the samples into high-risk and low-risk groups. Our model was trained and tested using a unique dataset obtained from the (KAUH) hospital in Jordan, consisting of well-balanced metadata from 120 patients with kidney issues. Our work demonstrated accuracy results ranging from 90% to 98%. Ultimately, specialists can utilize our model as an additional tool to enhance the speed and accuracy of patient diagnoses

关键词： Logistic regression Analytical models Accuracy Predictive models Genetics History Reliability

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Assessment and recognition in technical massive open on-line courses with and without on-line laboratories 14

Assessment and recognition in technical massive open on-line...

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14th IEEE Global engineering Education Conference, EDUCON 2023

作者： Martin, Sergio Castro, Manuel Sancristobal, Elio Diaz, Gabriel Garcia-Loro, Felix Perez, Clara Quintana, Blanca Gil, Rosario Plaza, Pedro Lopez, Africa Carro, German Robles, Antonio Tobarra, Llanos Rodriguez-Artacho, Miguel Ruiperez-Valiente, Jose A. Universidad Nacional de Educación A Distancia Electrical and Computer Engineering Department Madrid Spain Universidad Nacional de Educación A Distancia Dep. Sistemas de Comunicación y Control Madrid Spain Universidad Nacional de Educación A Distancia Dep. Lenguajes y Sistemas Informáticos Madrid Spain Masschusetts Institute of Technology Teaching Systems Lab United States

ISBN: (纸本)9798350399431

This paper describes the organization and results of several MOOCs delivered about technical topics (i.e., digital electronics, VHDL design on FPGAs, open education and OERs repositories and the use of STEAM technologies to encourage diversity and inclusion), where online laboratories have been used in some of them. An analysis of the enrollment, students participating on tasks or quizzes, drop-out rate and certifications requests have been done. © 2023 IEEE.

关键词： Drops

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Exploring Genetic Programming Models in computer-Aided Diagnosis of Skin Cancer Images

Exploring Genetic Programming Models in Computer-Aided Diagn...

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Congress on Evolutionary Computation

作者： Qurrat UI Ain Harith Al-Sahaf Bing Xue Mengjie Zhang School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand Dep. of Cybersecurity Engineering College of Engineering and Info. Technology AlShaab University Baghdad Iraq

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Extracting important information from complex skin lesion images is vital to effectively distinguish between different types of skin cancer images. In addition to providing high classification performance, such computer-aided diagnostic methods are needed where the models are interpretable and can provide knowledge about the discriminative features in skin lesion images. This underlying information can significantly assist dermatologists in identifying a particular stage or type of cancer. With its flexible representation and global search abilities, Genetic Programming (GP) is an ideal learning al-gorithm to evolve interpretable models and identify important features with significant information to discriminate between skin cancer classes. This paper provides an in-dep.h analysis of a recent GP-based feature learning method where different well-developed feature descriptors are integrated into the learning algorithms to extract high-level features for skin cancer image classification. The study explores the effectiveness of utilizing feature learning for this complex task and designing program structure to suit the problem domain as it has shown promising results compared to commonly used feature descriptors and an existing GP-based feature learning method developed for general image classification. This study analyzes the GP-evolved models to identify the prominent features and most effective feature descriptors important for the classification of these skin cancer images. The evolved models are interpretable, they provide knowledge that can assist dermatologists in making diagnoses in real-time clinical situations by identifying prominent skin cancer characteristics captured by the feature descriptors and learned during the evolutionary process.

关键词： Representation learning Visualization Computational modeling Genetic programming Feature extraction Skin Lesions

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GUI-PV Application Tool for Teaching Performance of PV System using MATLAB-Graphical User Interface Environment 1

GUI-PV Application Tool for Teaching Performance of PV Syste...

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1st IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer engineering, MI-STA 2021

作者： Almaktoof, Ali Shebani, Nafaa Elfallah, Ali Cape Peninsula University of Technology Electrical Electronics and Computer Engineering Dep. Cape Town South Africa

ISBN: (纸本)9781665418560

A MATLAB-Graphical User Interface (GUI) based educational software which could aid in the analysis and education of photovoltaic (PV) modules is presented in this paper. The MATLAB-GUI for studying the PV module based on the evaluation of the current, voltage and power characteristics as well as performance evaluation of the PV module under different values of radiation and temperature are presented. The Graphical User Interface-Photovoltaic (GUI-PV) application is developed to simulate the characteristics of PV module and show the effects of temperature and solar radiation on the PV modules. In addition, the GUI-PV can provide PV module specifications and parameters including maximum power, current and voltage, fill factor and efficiency,...etc. Two GUI-PV models are developed for PV module characteristics and performance evaluation of solar module. Testing of the GUI-PV platform exposes that students who are trained using GUI-PV software easily understood the concept of a solar PV panel and defined the maximum power of the solar photovoltaic modules and some other parameters. © 2021 IEEE.

关键词： MATLAB

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Automated Crop Harvesting, Growth Monitoring and Disease Detection System for Vertical Farming Greenhouse 3

Automated Crop Harvesting, Growth Monitoring and Disease Det...

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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Jayasekara, Charith Banneka, Sajani Pasindu, Gihan Udawaththa, Yukthi Wellalage, Sasini Abeygunawardhane, Pradeep K.W. Sri Lanka Institute of Information Technology Dep. of Computer Systems Engineering Malabe10115 Sri Lanka

ISBN: (纸本)9781665408622

Greenhouses are a type of cultivation method used to optimize the production of crops by using controlled climatic conditions and other external factors. They are widely used in agriculture both globally and locally. Vertical farming is the modern practice of growing crops in vertically stacked layers in warehouses that aims to optimize and develop plant growth by using controlled environment agriculture. This type of agriculture concept is practiced in Sri Lanka at present. This research paper proposes a robot which consists of a specific navigation system and harvesting mechanism that can be used inside greenhouses. The proposed system can be implemented to harvest lettuce in the vertical farming greenhouses where lettuce needs to be harvested with care to ensure the supermarket quality. The above-mentioned system helps to detect the diseases of plants also where a lot of time can be saved, and less effort is made with the use of this type of implementation. © 2021 IEEE.

关键词： Greenhouses

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Bayesian Network Structure Discovery Using Antlion Optimization Algorithm.

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2022年第1期7卷 46-65页

作者： Awla, Hoshang Qasim Kareem, Shahab Wahhab Mohammed, Amin Salih Computer science department Faculty of Science Soran University Erbil Iraq Dep. of Technical Information Systems Engineering Erbil Technical Engineering College Erbil Poly-technic University Erbil Iraq Department of Information Technology College of Engineering and Computer Science Lebanese French University Kurdistan Region Erbil Iraq

Bayesian networks have recently been used for discovering an optimal learning structure in machine learning. Bayes networks can describe possible dep.ndencies of explanatory variables. As a novel approach to studying the structure of a Bayesian network, the authors present the Antlion Optimization Algorithm (ALO). In the algorithm;deletion, rewind, insertion, and change are utilized to produce ALO to reach the best hull solution. Essentially, the technique used in the ALO algorithm imitates the antlions’ behaviors while hunting. The suggested approach is contrasted with simulated Annealing, Simulated Annealing Hybrid Bee, Greedy Search Hybrid Bee, optimization inspired by Pigeon, and greedy search using the BDe Score function. The researchers also studied the representation of the confusion matrix of these techniques using different reference data sets. The findings of the assessments reveal that the proposed algorithm works better than the other algorithms and has better consistency and score values. As shown by the experimental evaluations, the proposed method has a more reliable performance than other algorithms (including the production of excellent scores and accuracy values). © 2022. All Rights Reserved.

关键词： Antlion Optimization Bayesian Network Hunting Search Local & Global Search Metaheuristics Search and Score Structure Learning

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Machine learning algorithms in Bigdata Analysis and its applications: A review 4

Machine learning algorithms in Bigdata Analysis and its appl...

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4th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2022

作者： Khoshaba, Farah Kareem, Shahab Awla, Hoshang Mohammed, Chnar Erbil Polytechnic University Technical Engineering College Information System Engineering Department Erbil Iraq Lebanese French University College of Engineering and Computer Science Dep. of Information Technology Kurdistan Erbil Iraq Soran University College of Science Department of Computer Science Erbil Iraq

ISBN: (纸本)9781665468350

A wide range of disparate variety of heterogeneous and even disparate data sources has been integrated into the computer science research principles through the assistance of Artificial Intelligence (AI) and Machine Learning (ML) delivering excellent results in accuracy and data quality. Even so, taking machine learning approaches to very broad and dynamic datasets is both logistically and computationally costly. Some people are generating so much data regularly, which means that advanced analytics platforms have greater importance than ever before. Spark Machine Learning has extensive capabilities for a variety of ML activities such as regression, grouping, dimension reduction, and extraction of rules. While it has numerous datasets of high quality and consistency of structure. The specification of the MapReduce programming model was intended for the massive density of large-volume computation. It accomplishes this by splitting the job into several well-defined sub-sized units. There are various sets of data mining frameworks that link to a distributed device. Hadoop serves as the infrastructure for the distributed system. Several resources claim that Mahout will scale to handle massive datasets. This article discusses and demonstrates the use of algorithms and their significant impact on Bigdata, as well as a comparison between their implementations among Apache Spark and Apache Mahout. © 2022 IEEE.

关键词： Big data

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