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Assessment of Combined Cooling, Heating, and Power (CCHP) systems for Energy Efficiency in Administrative Buildings in Hot and Dry Climate 28

Assessment of Combined Cooling, Heating, and Power (CCHP) Sy...

引用

28th International Electrical Power Distribution Conference, EPDC 2024

作者： Zeynali, Shahrzad Nazififard, Mohammad Hashemi-Dezaki, Hamed University of Kashan Dep. of Energy Systems Engineering Kashan Iran University of Kashan Dep. of Electrical and Computer Engineering Kashan Iran

ISBN: (纸本)9798350385502

Combined cooling, heating, and power (CCHP) systems have garnered significant attention as a cost-effective solution for reducing energy consumption in buildings due to their ability to provide higher thermal efficiency compared to single power generation systems. This study focuses on modeling and evaluating the energy consumption of an administrative building within the University of Kashan, alongside the output values of a nearby CCHP power plant. The consumption values of the university's central administrative building have been obtained using available measuring devices. Also, the validity of the modeling results has been assessed using Design-Builder software. Additionally, the study investigates the role of the CCHP system in the building in terms of energy production and consumption by comparing the building's consumption with the production of CCHP electricity and heat. The simulation results indicate that the total annual energy consumption of the office building is 182.67 kWh/m2, while the values recorded by the engine room measuring devices show 199.75 kWh/msystems. This suggests a potential discrepancy between the simulated and measured values, with the simulated value being approximately ten percent lower than the measured value. Possible factors contributing to this variance include discrepancies in people's activity models used in the simulation software and the actual consumption behavior patterns. Furthermore, the study highlights that the 1 MW CCHP system can meet the thermal and electrical needs of a 3-story administrative building, such as the central administrative building of the University of Kashan, while also being able to supply excess electrical energy to the grid. This underscores the potential of CCHP systems in effectively addressing energy demands in buildings. © 2024 IEEE.

关键词： Office buildings

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CNN-based Human Activity Recognition on Edge Computing Devices

CNN-based Human Activity Recognition on Edge Computing Devic...

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2023 IEEE International Conference on Omni-Layer Intelligent systems, COINS 2023

作者： Singh, Amandeep Margaria, Tiziana Demrozi, Florenc University of Limerick Dep. of Computer Science and Information Systems Ireland University of Stavanger Dep. of Electrical Engineering and Computer Science Norway

ISBN: (纸本)9798350346473

Human Activity Recognition (HAR) is a research area that involves wearable devices integrating inertial and/or physiological sensors to classify human actions and status across various application domains, such as healthcare, sports, industry, and entertainment. However, executing HAR algorithms on remote devices or the cloud can lead to issues such as latency, bandwidth requirements, and energy consumption. Transitioning towards Edge HAR can be a more effective and versatile solution, overcoming the challenges of traditional HAR techniques. We present a novel HAR model for computation on edge devices: we design a Convolutional Neural Network (CNN) Deep Learning approach and compare its performance with cloud-computing HAR models. The paper is accompanied by a self-collected dataset. The experiments on this dataset demonstrate that the proposed edge computing model achieves promising results (\geq 92 %) in terms of Precision, Recall, and Fl-score. Furthermore, the model exhibits significantly reduced latency, with only 117 ms, and utilizes minimal memory, with a peak of 18.8 Kb RAM and 956 Kb Flash memory. © 2023 IEEE.

关键词： Edge computing

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Two-Rated-Power, Dual-Winding, Three-Phase Induction Motor

Two-Rated-Power, Dual-Winding, Three-Phase Induction Motor

引用

2024 International Conference on Electrical Machines, ICEM 2024

作者： Ferreira, Fernando J. T. E. Institute of Systems and Robotics University of Coimbra Dep. of Electrical and Computer Engineering Coimbra3030-250 Portugal

ISBN: (纸本)9798350370607

This paper proposes using star-delta and star-series connection modes in three-phase dual-winding squirrelcage induction motors to have available 100% and 55% of the original rated power for a given supply line-to-line voltage. The connection mode can be changed on a nine-terminal block. The experimental results obtained with a commercial 4-kW, 400-V, 50-Hz, 4-pole induction motor whose stator windings were redesigned to provide 4-kW and 2.2-kW output powers are presented and validate the proposed solution. Two-rated-power motors with the proposed output power ratio could be used as spare motors to replace faulty/damaged motors with the same frame size. The proposed solution allows fine-tuning the motor power to the actual maximum power required by the driven application/load and can be considered cost-effective as it may lead to significant gains in the motor efficiency and power factor when the low-power mode is adopted. In many cases, it may avoid the underloaded operation of induction motors, while keeping their original rated power available. © 2024 IEEE.

关键词： Microelectronics

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From Manual to Semi-Automated Safety and Security Requirements engineering: Ensuring Compliance in Industry 4.0 50

From Manual to Semi-Automated Safety and Security Requiremen...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

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

ISBN: (纸本)9781665464543

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. © 2024 IEEE.

关键词： Requirements engineering

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Towards Unveiling Vulnerabilities and Securing IoT Devices: An Ontology-Based Approach 29

Towards Unveiling Vulnerabilities and Securing IoT Devices: ...

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29th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2024

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

ISBN: (纸本)9798350361230

In the rapidly expanding landscape of Internet of Things (IoT), sensors have emerged as pivotal components, playing a critical role in sensing and data collection. However, this crucial function also renders them susceptible to potential threats such as unauthorized access and security breaches. This paper investigates the vulnerabilities that lead to these threats and offers a comprehensive set of best practices aimed at fortifying the security of IoT devices. We highlight key measures such as secure device design, robust authentication mechanisms, encrypted communication protocols, regular updates, and emphasize the importance of collaborative efforts among stakeholders using an ontological approach. An Ontology provides a structured framework for organizing knowledge, facilitating clearer communication, efficient data management, and enhanced decision-making. Thus, this paper contributes to the development of a more secure and resilient IoT ecosystem. © 2024 IEEE.

关键词： Ontology

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IT Security Solutions for IT/OT Integration: Identifying Gaps and Opportunities 29

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

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29th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2024

ISBN: (纸本)9798350361230

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. © 2024 IEEE.

关键词： Data acquisition Industry 4.0 IT Security Solutions IT/OT Integration Operation Technology

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Feature Selection for Robust Spoofing Detection: A Chi-Square-based Machine Learning Approach 2

Feature Selection for Robust Spoofing Detection: A Chi-Squar...

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2nd International engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Al-Na'amneh, Qais Aljaidi, Mohammad Gharaibeh, Hasan Nasayreh, Ahmad Emhamed Al Mamlook, Rabia Almatarneh, Sattam Alzu'bi, Dalia Suliman Husien, Abla Applied Science Private University Dep. of Cyber Security Amman Jordan Zarqa University Dep. of Computer Science Zarqa Jordan Yarmouk University Dep. Computer Science Irbid Jordan Trine University Dep. of Business Administration Indiana United States University of Zawia Department of Industrial Engineering Tripoli Libya Zarqa University Dep. of Data Science and Artificial Intelligence Zarqa Jordan Concordia University Dep. Information and Systems Engineering Montreal Canada

ISBN: (纸本)9798350373363

Nowadays, the Internet of Things (IoT) system is vulnerable to spoofing attacks that can easily where attackers can easily pose as a legal entity of the network. A 'spoofing attack' refers to a type of cyber-attack when an attacker purposefully impersonates or masquerades as someone or something else to deceive the target or obtain unauthorized access to systems, information, or resources. In such attacks, the attacker alters their name, IP address, or other attributes to fool the victim into thinking they are engaging with a legitimate entity. Spoofing attacks can take place via a variety of channels, including, ARP and DNS spoofing. Therefore. Spoofing attacks can have serious consequences. We proposed a new approach based on three machine learning models LightGBM, Gradient Boost, and XGBoost to classify attacks on spoofing, we used Chi-square to select the best features to get the highest performance, and we demonstrated that the results using Chi-square achieved higher results than without Chi-square and improved the result with a rate three percent of accuracy. In terms of results, LightGBM outperformed other models by achieving 89%, 91 %, 87 %, and 89 % for accuracy, precision, recall, and f1-score, respectively. This shows the potential and efficiency of Chi-square to achieve the best performance by selecting the best features, thus providing a secure system and identifying cyber-attacks on systems. © 2023 IEEE.

关键词： Internet of things

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Logistic Regression Classification for Assessing the Risk of Kidney Tumor 2

Logistic Regression Classification for Assessing the Risk of...

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2nd International engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Alzubi, Dalia Al Mamlook, Rabia Emhamed Nasayreh, Ahmad Aljaidi, Mohammad Alazab, Rami Gharaibeh, Hasan Al-Naamneh, Qais Concordia University Dep. Information and Systems Engineering Montreal Canada Trine University Dep. of Business Administration IN United States University Zawia Department of Industrial Engineering Tripoli Libya Yarmouk University Dep. Computer Science Irbid Jordan Zarqa University Dep. of Computer Science Zarqa Jordan Jordan University of Science and Technology Dep. Surgery and Urology Irbid Jordan Applied Science Private University Dep. of Cyber Security Amman Jordan

ISBN: (纸本)9798350373363

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 © 2023 IEEE.

关键词： Tumors

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Knowledge Representation of Asset Information and Performance in OT Environments 28

Knowledge Representation of Asset Information and Performanc...

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28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

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

ISBN: (纸本)9798350339918

To make a management decision for the Operational Technology (OT) environment, obtaining OT asset information from a plant is essential. Key Performance Indicators (KPIs) can play a crucial role in evaluating the performance of the manufacturing process, minimizing production costs, assessing process effectiveness, and investigating various settings to improve performance, operations, and quality in OT. This paper aims to provide a detailed examination of how Asset Information Mining Sources (AIMSs) support the process for acquiring both static and runtime OT asset information. Once we have obtained accurate OT asset information, we can calculate the KPIs and incorporate them into a knowledge representation using an ontology-based model. This model can assist the management team in making informed decisions and provide a roadmap for exploring further automation possibilities. © 2023 IEEE.

关键词： Knowledge representation

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Fine-Tuning Pre-Trained Models for Automated Analysis of Ophthalmic Imaging in Diagnosing Eye Diseases 24

Fine-Tuning Pre-Trained Models for Automated Analysis of Oph...

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24th International Arab Conference on Information Technology, ACIT 2023

作者： Al Mamlook, Rabia Emhamed Nasayreh, Ahmad Gharaibeh, Hasan Aljaidi, Mohammad Al-Na'amneh, Qais Alzu'bi, Dalia Bzizi, Hanin Abdeltawab, Abdelrahman Trine University Dep. of Business Administration Indiana United States University Zawia Department of Industrial Engineering Tripoli Libya Yarmouk University Dep. Computer Science Irbid Jordan Zarqa University Dep. of Computer Science Zarqa Jordan Applied Science Private University Dep. of Cyber Security Amman Jordan Concordia University Dep. Information and Systems Engineering Montreal Canada Western Michigan University Dep of Biomedical Science KalamazooMI49008 United States Bani Sweif University Faculty of Pharmacy Bani Suwayf Egypt

ISBN: (纸本)9798350384307

This paper searches into the convergence of such ad-vanced techniques with architectures like DenseNet201, VGG16, InceptionResNetV2, and NasNetMobile. Our focus centers on harnessing deep learning capabilities for the precise detection of eye ailments. Preliminary findings spotlight a notable uplift in diagnostic precision. Among the tested algorithms, the Nas-NetMobile architecture emerged superior, boasting an impressive accuracy rate of 92% on the chosen dataset. Through this work, we extend a significant advancement in the realm of automated eye disease diagnosis, potentially equipping medical professionals with sharper, swifter diagnostic tools. The outcomes presented herein could play a seminal role in sculpting more sophisticated and precise diagnostic approaches within ophthalmology. © 2023 IEEE.

关键词： Deep Learning Eye Disease Fine Tuning Transfer Learning

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