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Doctoral Consortium at International Semantic Web Conference 2024, ISWC-DC 2024

作者： Hoseini, Sayed Department of Electr. Engineering and Computer Science Niederrhein University of Applied Sciences Krefeld Germany

Managing large volumes of data poses significant challenges due to the variety of formats, distribution across departments, and different governance structures within organizations. In research and industry environments, this complexity is compounded by the need for streamlined data handling processes to support automated workflows and machine learning (ML) applications. Integrating implicit contextual knowledge alongside data artifacts is critical, especially for non-expert users accessing the data. Data lakes provide a scalable solution by aggregating raw data from disparate sources with minimal upfront integration costs. However, without proper integration, data analysis and interpretation is hindered, rendering the data lake effectively inoperable. This PhD research addresses these challenges by applying semantic data management (SDM) techniques inside a semantic data lake. While initial milestones have been achieved through a systematic literature review and a concrete implementation, further efforts lie ahead. First, the emergence of large language models offers numerous opportunities for automating previously manual processes. Leveraging these models can significantly improve the efficiency of common SDM tasks. Second, extending the application of SDM techniques to data analytics can facilitate the integration of diverse data sources into ML pipelines. Ultimately, we aim to bridge the gap between Big Data and Semantic Web technologies, anticipating the development of advanced semantic data lake solutions in the foreseeable future. © 2024 Copyright for this paper by its authors.

关键词： Data assimilation

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Towards Carbon-Neutral Healthcare Facilities: Design and Evaluation of a Renewable Energy Microgrid 10

Towards Carbon-Neutral Healthcare Facilities: Design and Eva...

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10th IEEE International Conference on Power Systems, ICPS 2023

作者： Salsabil, Samin Joy, Joyonta Das Reza, Abtahi Noor, Nahiyan Bin Chittagong University of Engr. & Tech Department of Electr. & Electronic Engineering Bangladesh Temple University Department of Electr. & Computer Engineering United States University of Arkansas at Little Rock Department of Information Science United States

ISBN: (纸本)9798350318739

The United Nations (UN) Sustainable Development Goals (SDGs) demand clean and reliable energy delivery. Here, we present a comprehensive study focusing on the design, analysis, and social impact assessment of a microgrid system tailored for a hospital. The microgrid is designed to support both grid-connected and islanded operation modes, incorporating PV panels, wind turbines, combined heat and power (CHP) systems, and energy storage. In this case study, our objective was to ensure an uninterrupted power supply during grid blackouts, achieving a minimum of 75% electr.city demand coverage for at least 5 consecutive days. A detailed cost analysis is conducted, considering alternative design configurations and the influence of discount rates on the economic feasibility of the system. To promote environmental sustainability, a constraint is imposed on carbon emissions, aiming to keep them below 50% of the grid emissions. Additionally, a comprehensive social impact assessment is performed to evaluate the effects of the proposed microgrid project on various stakeholders in the surrounding area. The integrated approach of renewable energy integration, cost analysis, environmental constraints, and social impact assessment contributes to a holistic evaluation, enabling the successful implementation of microgrid systems in similar contexts. © 2023 IEEE.

关键词： Sustainable development

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Strategic Utilization of Dispatchable Loads and Nodal Reserves for Improved Reserve Deliverability 6

Strategic Utilization of Dispatchable Loads and Nodal Reserv...

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6th International Conference on electr.cal Information and Communication Technology, EICT 2023

作者： Das Joy, Joyonta Salsabil, Samin Reza, Abtahi Noor, Nahiyan Bin Chittagong University of Engr. & Tech. Department of Electr. & Electronic Engineering Bangladesh Temple University Department of Electr. & Computer Engineering United States University of Arkansas Department of Information Science Little Rock United States

ISBN: (纸本)9798350370836

In recent years, it has become increasingly clear that simply having sufficient capacity to meet demand is not adequate. Ensuring the deliverability of reserves in contingency scenarios is a significant challenge, further complicated by rising uncertainties and the integration of Distributed Energy Resources (DERs) into the system. This research introduces an algorithm for detecting deliverability issues under contingency conditions and provides a comparative analysis between dispatchable loads and the availability of operating reserves within the network. Our methodology addresses this challenge by sequentially tackling dispatchable loads, only invoking the operating reserve when conventional sources cannot deliver during a contingency. Our findings indicate that, in a given system, certain nodes can deliver reserve with less need for flexibility, while adhering to operational constraints, thus reducing costs by approximately 40-60%. This study aids in pinpointing optimal nodes for reserves, requiring minimal adjustments or costs, and in identifying areas to avoid, where ensuring flexibility does not necessarily ensure the deliverability of reserves or may lead to elevated costs. © 2023 IEEE.

关键词： Energy resources

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AI-based Detection of Stress using Heart Rate Data obtained from Wearable Devices 3

AI-based Detection of Stress using Heart Rate Data obtained ...

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3rd International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2023

作者： Mandal, Anurag Paradkar, Manasi Panindre, Prabodh Kumar, Sunil Dept. of Electr. Computer Engineering New York University Brooklyn United States Dept. of Mechanical Engineering New York University Brooklyn United States

Stress, a pervasive issue in today's world, significantly impacts mental and physical health. With approximately 20% of adults experiencing mental health disorders each year, and depression affecting over 260 million people globally, effective stress management is crucial. Chronic stress is also associated with physical ailments, including cardiovascular diseases, the leading global cause of death. In parallel, the wearable device market has witnessed substantial growth, with over 300 million units sold worldwide in 2021. These devices offer a unique opportunity to collect physiological and behavioral data, making them invaluable for stress detection and prediction. Combining artificial intelligence (AI) and wearables, this paper explores their accuracy in capturing vital data and the potential to revolutionize stress management, ultimately improving mental and physical well-being. In this paper, the ensemble-based classifier known as the Histogram-based Gradient Boosting Classifier detected stress from heart rates with an accuracy of 73%. This establishes the independent nature of Heart Rates for stress prediction, emphasizing that there is no need for sophisticated ECG and HRV readings. This opens up the possibility of using sensors as used in wearable devices to effectively detect stress in our day-to-day lives. © 2023 Elsevier B.V.. All rights reserved.

关键词： Artificial intelligence

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AI-based Detection of Signs of Depression from Physiological Data obtained from Health Trackers 2

AI-based Detection of Signs of Depression from Physiological...

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Panindre, Prabodh Mandal, Anurag Paradkar, Manasi Kumar, Sunil New York University Dept. of Mechanical Engineering Brooklyn United States New York University Dept. of Electr. & Computer Engineering Brooklyn United States

ISBN: (纸本)9781665456302

According to the National Institute of Mental Health, Major Depressive Disorder affected an estimated 21.0 million American adults in 2020, which represents 8.4% of the U.S. population aged 18 or older in a given year. Even though the percentage is substantial, it reflects only the diagnosed cases. Most depression cases remain undiagnosed and thus untreated. Real-time monitoring of physiological indicators of depression using wearable health monitoring devices can help increase the chances of early detection and eventual treatment. In this research, various Artificial Intelligence algorithms are developed to look for signs of stress and anomalies in activity patterns from the data captured by wearable health devices. The Random Forest algorithm performed well in detecting depression from users' activity levels, while the K-Nearest Neighbours algorithm detected stress, one of the key indicators of depression, with an accuracy of 96.2% from Heart Rate variability. This research takes advantage of real-time access to one's physiological data to minimize the number of undiagnosed depression cases. © 2023 IEEE.

关键词： Wearable technology

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Hardware Acceleration of Transformer Networks using FPGAs

Hardware Acceleration of Transformer Networks using FPGAs

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2022 Panhellenic Conference on electr.nics and Telecommunications, PACET 2022

作者： Tzanos, Georgios Kachris, Christoforos Soudris, Dimitrios Ntua Dep. of Electr. and Computer Engineering Athens Greece ICCS-NTUA Duth Greece

ISBN: (数字)9798350399585

ISBN: (纸本)9798350399585

Natural Language Processing (NLP) allows program computers to process and analyze large amounts of natural language data. In the last few years, NLP has shown tremendous growth, with many organizations presenting models such as BERT (Bidirectional Encoder Representations from Transformers), GPT2 (Generative Pre-trained Transformer 2), GPT3 (Generative Pre-trained Transformer 3), etc. The cornerstone of these models and the reason for the growth of the NLP is mainly due to the Transformer networks. However, very few architectures have been presented, for the acceleration of the Transformer networks using FPGAs. In this paper, we propose a novel architecture for Transformer Networks optimized on FPGAs. The performance evaluation on a Xilinx Alveo U200 FPGA achieved up to 80.5x speed-up over a single-core CPU and up to 2.3x speedup over a 40-thread Xeon CPU running BERT model. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Sigmoid Functions Applied in Proportional + Resonant Reference for Current Harmonic Sharing 8

Sigmoid Functions Applied in Proportional + Resonant Referen...

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8th Southern Power electr.nics Conference and the 17th Brazilian Power electr.nics Conference, SPEC / COBEP 2023

作者： Zilli, Bruno Meneghel Silva, Lucas Jonys Ribeiro Von Rondow Campos, Márcio De Aguiar, Cassius Rossi MacHado, Ricardo Quadros University of Sao Paulo Dept. of Electr. & Comput. Engineering Sao Carlos Brazil Federal University of Technology-Paraná Dept. of Computer Engineering Toledo Brazil

ISBN: (纸本)9798350321128

The concept of distributed generation and microgrids allowed different alternative of power sources to be integrated to the main grid, made possible due to the evolution and improvement of power converters, like DC-AC inverters. This paper proposes an alternative harmonic current sharing methodology without communication between the inverters in the microgrid. Sigmoid functions designed for each frequency in each inverter manage the amplitudes of currents references of PR controller, assigning the appropriate amount to each element taking into account the capacity and availability of each inverter. The grid connected system is composed by two VSIs operanting in parallel at the point of common coupling, as well as the non-linear load. The results presented a harmonic compensation evaluating VSIs with different power capacities. In a first scenario, the VSIs were designed for only supply active power, and in a second scenario, the converters provided almost all the harmonic needed for a non-linear load. © 2023 IEEE.

关键词： harmonic sharing Microgrid Voltage Source Inverters

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Analyzing the Impact of Temperature Variations on the Performance of Thermoelectr.c Generators 2

Analyzing the Impact of Temperature Variations on the Perfor...

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2nd International engineering Conference on electr.cal, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Yahya, Adel E. M. Sarreb, Rebha Daw Yahya, Khalid Attar, Hani Aldababsa, Mahmoud Hafez, Mohamed Sabratah University Dept of Computer Engineering Regdalen Libya Sabratah University Dept of Electrical Engineering Regdalen Libya Istanbul Gelisim University Dept of Elec. and Electr. Engineering Istanbul Turkey Zarqa University Jordan College of Engineering University of Business and Technology Dept of Energy Engineering Jeddah21448 Saudi Arabia Nisantasi University Dept of Elec. and Electr. Engineering Istanbul Turkey INTI-IU-University Faculty of Engineering Feqs Nilai Malaysia

ISBN: (纸本)9798350373363

Recent advancements in renewable energy technologies have brought Thermoelectr.c Generators (TEGs) to the forefront, primarily due to their ability to efficiently convert waste thermal energy into electr.cal power across multiple power levels. This study delves into the environmental benefits and operational efficiencies of TEGs, highlighting their zero-emission, silent operation, and low maintenance requirements. A significant portion of this research is dedicated to exploring the influence of temperature differential (ΔT) on the efficacy of TEGs, as temperature is a crucial factor in the energy conversion process. The electr.cal representation of TEGs is modeled as a voltage source in series with an internal resistance, while its thermal aspect comprises parallel-connected p- and n-type thermocouples. The study aims to meticulously analyze the behavior of TEG models under various thermal gradients and to scrutinize their electr.cal characteristics under different load conditions. This is achieved through comprehensive experimental methodologies, with the findings underscoring the impact of temperature variations on both hot and cold sides of TEGs on all electr.cal parameters. It is observed that an increase in the temperature difference results in a corresponding rise in both the maximum power output and the open-circuit voltage. In essence, the efficiency of TEGs is noted to improve with a higher and more stable temperature differential. © 2023 IEEE.

关键词： Thermocouples

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AI-based Detection of Stress using Heart Rate Data obtained from Wearable Devices

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Procedia computer Science 2023年 230卷 749-759页

作者： Anurag Mandal Manasi Paradkar Prabodh Panindre Sunil Kumar Dept. of Electr. Computer Engineering New York University Brooklyn USA Dept. of Mechanical Engineering New York University Brooklyn USA

Stress, a pervasive issue in today's world, significantly impacts mental and physical health. With approximately 20% of adults experiencing mental health disorders each year, and depression affecting over 260 million people globally, effective stress manage- ment is crucial. Chronic stress is also associated with physical ailments, including cardiovascular diseases, the leading global cause of death. In parallel, the wearable device market has witnessed substantial growth, with over 300 million units sold worldwide in 2021. These devices offer a unique opportunity to collect physiological and behavioral data, making them invaluable for stress detection and prediction. Combining artificial intelligence (AI) and wearables, this paper explores their accuracy in capturing vital data and the potential to revolutionize stress management, ultimately improving mental and physical well-being. In this paper, the ensemble-based classifier known as the Histogram-based Gradient Boosting Classifier detected stress from heart rates with an accuracy of 73%. This establishes the independent nature of Heart Rates for stress prediction, emphasizing that there is no need for sophisticated ECG and HRV readings. This opens up the possibility of using sensors as used in wearable devices to effectively detect stress in our day-to-day lives.

关键词： Mental Health Stress Anxiety Fitness Trackers Artificial Intelligence

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Towards Carbon-Neutral Healthcare Facilities: Design and Evaluation of a Renewable Energy Microgrid

Towards Carbon-Neutral Healthcare Facilities: Design and Eva...

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International Conference on Power Systems (ICPS)

作者： Samin Salsabil Joyonta Das Joy Abtahi Reza Nahiyan Bin Noor Department of Electr. & Electronic Engineering Chittagong University of Engr. & Tech Bangladesh Department of Electr. & Computer Engineering Temple University USA Department of Information Science University of Arkansas at Little Rock USA

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