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49th IEEE Photovoltaics Specialists Conference, PVSC 2022

作者： Chaudhary, Jatin Bhattacharya, Swastik Heikkonen, Jukka Kanth, Rajeev University of Turku Department of Computing Turku Finland University of Colorado Department of Electrical Computer and Energy Engineering Boulder United States School of Information Technology Savonia University of Applied Sciences Kuopio Finland

ISBN: (纸本)9781728161174

Increasing population and industrialization haveled to an uptick in energy requirements. Many traditional energy sources are not anymore attractive due to climate change, instead, the interest has turned to power generation from renewable sources, such as wind energy, hydro-power, and solar energy. The wide availability of sunlight and simplicity in converting sunlight to electricity has led to the search for synthesized semiconductors that give high efficiency in this conversion. A family of such semiconductors attains the perovskite structure, the most established being Methyl Ammonium Lead Iodide. The shortcomings of this compound include lead poisoning, motivating the search for perovskite structures that have low electron band-gap and are stable. A family of such perovskite structures is compounds that attain an A2XY6 type structure. This paper demonstrates some methods that can be used to calculate the electron band-gap of such compounds. The metrics found from Support Vector Machine Regression and Random Forest Regression are compared and analyzed to propose a scalable model for predicting electron band-gap. © 2022 IEEE.

关键词： Support vector machines

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PVA-based KOH polymer gel electrolyte as a membrane separator for zinc-air flow battery

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Materials Today: Proceedings 2022年 64卷 1649-1654页

作者： Sankaralingam, Ram Kishore Seshadri, Satyanarayanan Sunarso, Jaka Bhatt, Anand I. Kapoor, Ajay Department of Applied Mechanics Indian Institute of Technology Madras Tamil Nadu Chennai 600036 India Research Centre for Sustainable Technologies Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Campus Sarawak Kuching 93350 Malaysia Faculty of Science Engineering and Technology Swinburne University of Technology Hawthorn VIC 3122 Australia CSIRO Energy Research Way Clayton VIC 3168 Australia Chinmaya Vishwavidyapeeth Veliyanad PO Ernakulam Kerala 682313 India

The anticipated future growth in the electric vehicle market is driving research and development of alternative battery technologies to lithium batteries. Among the explored batteries, metal-air batteries are an exciting option given their high potential energy density. Currently, metal-air flow batteries have received more attention over conventional metal-air batteries due to their ability to reduce metal passivation. The separator for a metal-air flow battery should be both ion-conductive and partially hydrophobic to favour the ionic movement and suppress the electrolyte flooding on the cathode. In this work, we investigate the use of polyvinyl alcohol (PVA)-based potassium hydroxide (KOH) polymer gel electrolyte (KPGE) as a membrane separator for planar zinc-air flow battery (ZAFB). The KPGE samples, i.e., S20 and S33 with respective KOH wt.% of 20 and 33 were synthesised. Their surface morphology, porosity, tensile strength, and ionic conductivity were studied. Among the samples, S33 sample with higher KOH content showed higher ionic conductivity than S20 sample however the tensile strength of S33 sample was lower than the S20 sample. When tested in the battery, ZAFB with S33 sample demonstrated higher nominal voltage and improved polarisation characteristics when compared to the ZAFB with S20 sample. © 2022

关键词： Surface morphology

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A Framework to Preserve and Examine Pandemic-Healthcare-Data Using IoMT 1

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International Conference on Big Data and Cloud computing, ICBDCC 2021

作者： Kadry, Seifedine Rajinikanth, Venkatesan Faculty of Applied Computing and Technology Noroff University College Kristiansand4608 Norway Department of Electronics and Instrumentation Engineering St. Joseph’s College of Engineering OMR Tamilnadu Chennai600119 India

ISBN: (数字)9789811921773

ISBN: (纸本)9789811921766

Resent advancement in networking and communication system helped to enhance various domains, including the healthcare. The invention of the cloud computing, Internet of medical things (IoMT), and artificial intelligence (AI) invented the Healthcare 4.0, which supports the monitoring and examination of a variety of medical information using the digital techniques. This research aims to develop a diagnostic framework for the pandemic-healthcare-data (PHD) using the IoMT scheme. The proposed framework considers the examination of the patients admitted with the COVID-19 infection and supports the following procedures;(i) collecting the preliminary information about the patient, (ii) collecting the disease information, (iii) getting the experts opinion regarding the treatment planning and implementation, (iv) monitoring the patient, and (v) preserving the disease information for future use. The proposed scheme is developed by considering the COVID-19 PHD, and the methodology employed is discussed with a chosen procedures. When this scheme is implemented, the preserved data can be used to develop a medical model, which supports a quick diagnosis and timely treatment to recover the patient. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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Explainable-AI for DoS Attacks Detection in 5G Network Using Deep Learning Models 5

Explainable-AI for DoS Attacks Detection in 5G Network Using...

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5th International Conference on Intelligent computing, Communication, Networking and Services, ICCNS 2024

作者： Albashayreh, Amjad Tashtoush, Yahya Aldosary, Abdallah Darwish, Omar Albalas, Firas The University of Jordan Department of Computer Science Amman Jordan Jordan University of Science and Technology Department of Computer Science Irbid Jordan Prince Sattam bin Abdulaziz University The Department of Computer Engineering Ar Riyadh Saudi Arabia Eastern Michigan University Information Security and Applied Computing Department YpsilantiMI48197 United States

ISBN: (纸本)9798350354690

With the emergence of the fifth-generation (5G) net-work, numerous revolutionary applications are enabled, including low-latency and machine-type communications. This great in-crease creates a broader security threat concerns, such as denial-of-service (DoS) attacks, which can disrupt network functionality. The complexity and decentralization of 5G networks create new vulnerabilities for adversaries, necessitating comprehensive security procedures to identify, mitigate, and prevent DoS attacks in 5G networks. This paper introduces a novel approach for DoS detection in 5G networks, utilizing deep learning and machine learning models, along with Local Interpretable Model-Agnostic Explanations (LIME), to interpret model predictions and identify the significant role of data features in detecting DoS attacks. The results revealed that the random forest model demonstrated superior recall of 99.98, while BiLSTM demonstrated exceptional performance with a recall of 98.02. © 2024 IEEE.

关键词： 5G mobile communication systems

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The Role of AI in Enhancing Decision-Making in Small and Medium Enterprises (SMEs)

The Role of AI in Enhancing Decision-Making in Small and Med...

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computing and Data Analytics (ICCDA), International Conference on

作者： Joefrelin C. Ines Marilou Q. Tolentino Jay C. Dela Cruz Rolando Estabillo Anthony M. Madlambayan Cirilo E. Mirano Juvy A. Vergara Department of Business Admnistration College of Economics and Business Administration University of Technology and Applied Sciences Shinas Oman DBA College of Business Systems Plus College Foundation Angeles City Philippines Department of IT College of Computing and Information Sciences University of Technology and Applied Sciences Shinas Oman

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542580

This study investigates the transformative role of artificial intelligence (AI) in enhancing decision-making processes within small and medium enterprises (SMEs). By focusing on AI applications such as predictive analytics, the research highlights how these tools improve operational efficiency, optimize resource management, and facilitate real-time decision-making. Employing a quantitative methodology, data was collected from 100 SMEs in the Angeles City, Philippines through structured surveys, which were analyzed using descriptive statistics, regression, and correlation techniques. The findings reveal that AI significantly enhances decision-making processes, leading to improved business outcomes, including increased operational efficiency and profitability. Notably, 75% of the surveyed SMEs reported using predictive analytics, resulting in heightened decision- aking speed and accuracy. This study underscores the critical importance of AI in empowering SMEs to navigate market challenges effectively and sustain competitive advantages in an increasingly data-driven business landscape.

关键词： Surveys Profitability Navigation Decision making Urban areas Real-time systems Resource management Artificial intelligence Predictive analytics Business

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Multilingual Knowledge Graph Completion via Multimodal Decouple and Relation-based Ensemble Inference 29

Multilingual Knowledge Graph Completion via Multimodal Decou...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Liu, Xiaojing Jiang, Xuesong Yi, Fengge Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

The objective of Multimodal Knowledge Graph Completion (MKGC) is to forecast absent entities within a knowledge graph by leveraging additional textual and visual modalities. Existing studies commonly utilize a singular relationship embedding to depict all modalities within an entity pair, thus connecting several relationships derived from diverse modalities. However, this coupling may introduce interference from conflicting information between modalities, as the relationships between modalities for a given entity pair can be contradictory. Moreover, existing Ensemble Inference methods fail to dynamically adjust modal weights based on their differences and importance, despite the varying contributions of different modalities. In this paper, we propose the Multimodal Decouple and Relation-based Ensemble inference (MDRE) model. For each modality, we construct corresponding relationship embeddings and build separate triple representations to avoid interferences among modalities. During the training phase, we employ confidence-constrained training with temperature scaling to alleviate conflicting information in textual and visual modalities. For inference, we utilize the Relation-based Ensemble Inference method to adjust modal weights at the relationship level, thus achieving improved prediction results. Experimental results on two datasets demonstrate that MDRE outperforms existing single-modal and multimodal knowledge graph completion methods in terms of performance. © 2023 IEEE.

关键词： knowledge graph knowledge graph completion multimodal fusion

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A multi-objective grey wolf optimizer for energy planning problem in smart home using renewable energy systems

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Sustainable Operations and Computers 2024年第1期5卷 88-101页

作者： Makhadmeh, Sharif Naser Al-Betar, Mohammed Azmi Al-Obeidat, Feras Alomari, Osama Ahmad Abasi, Ammar Kamal Tubishat, Mohammad Elgamal, Zenab Alomoush, Waleed Department of Data Science and Artificial Intelligence University of Petra Amman11196 Jordan College of Engineering and Information Technology Ajman University Ajman United Arab Emirates Department of Information Technology Al-Huson University College Al-Balqa Applied University P.O. Box 50 Al-Huson Irbid Jordan Department of Computer Science and Information Technology College of Engineering Abu Dhabi University Abu Dhabi United Arab Emirates College of Technological Innovation Zayed University Abu Dhabi United Arab Emirates School of Computing Skyline University College Sharjah United Arab Emirates School of Information Technology Skyline University College Sharjah P.O. Box 1797 United Arab Emirates Abu Dhabi United Arab Emirates

This paper presents the energy planning problem (EPP) as an optimization problem to find the optimal schedules to minimize energy consumption costs and demand and enhance users’ comfort levels. The grey wolf optimizer (GWO), One of the most powerful optimization methods, is adjusted and adapted to address EPP optimally and achieve its objectives efficiently. The GWO is adapted due to its high performance in addressing NP-complex hard problems like the EPP, where it contains efficient and dynamic parameters that enhance its exploration and exploitation capabilities, particularly for large search spaces. In addition, new energy and real-world resources based on solar renewable energy systems (RESs) are combined with the proposed GWO to enhance its performance and ensure the optimisation of EPP objectives. Furthermore, EPP is presented as a multi-objective planning problem to optimize all objectives simultaneously. To efficiently investigate the proposed method performance, the results obtained by the GWO with the RESs are compared in three stages: comparison with original methods without RESs, comparison with methods using RESs, and comparison with state-of-the-art. The obtained results proved the robust performance of the proposed method in handling EPP and optimizing its objectives. © 2024 The Author(s)

关键词： Multiobjective optimization

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Green Hydrogen Microgrid: Household Energy Management System Dedicated to a Hydrogen Storage-based Renewable-Energy-System

Green Hydrogen Microgrid: Household Energy Management System...

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2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022

作者： Ben Slama, Sami Zafar, Bassam Shafie, Emad Applied College King Abdelaziz University Saudi Arabia Faculty of Sciences Tunis-El Manar University Tunis Tunisia King Abdulaziz University Faculty of Computing & Information Technology Jeddah Saudi Arabia Applied College Umm Al-Qura Univrsity Makkah Saudi Arabia

ISBN: (数字)9781665470957

ISBN: (纸本)9781665470957

Green Hydrogen Microgrid (GHM) is a Hybrid renewable energy that could help the industries reach zero emissions in the future. This paper suggests a hybrid renewable energy system based on microgrids to achieve these goals. The recommended renewable hybrid power system has a PVG, FC, SC, and V2H to meet a set demand. During peak hours, the proposed design is grid-connected and uses solar energy. It makes use of it at night or in the shadow. SC assists FC when the load power is high. SC operates in this manner during load transients. Based on the settings, the RT-Home Energy Management System automatically switches between two modes using a multi-agent system (MAS). This reduces the amount of hydrogen used while keeping SOC at the same level. The scheduling strategy minimizes energy consumption by managing automation devices without network power. It can fulfill residential energy demands in sunny, cloudy, and rainy weather. RT-HEMS is used to meet GHM performance and load requirements. © 2022 IEEE.

关键词： Energy management

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Is score matching suitable for estimating point processes? 24

Is score matching suitable for estimating point processes?

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Haoqun Cao Zizhuo Meng Tianjun Ke Feng Zhou Center for Applied Statistics and School of Statistics Renmin University of China Data Science Institute University of Technology Sydney Center for Applied Statistics and School of Statistics Renmin University of China and Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing

ISBN: (纸本)9798331314385

Score matching estimators have gained widespread attention in recent years partly because they are free from calculating the integral of normalizing constant, thereby addressing the computational challenges in maximum likelihood estimation (MLE). Some existing works have proposed score matching estimators for point processes. However, this work demonstrates that the incompleteness of the estimators proposed in those works renders them applicable only to specific problems, and they fail for more general point processes. To address this issue, this work introduces the weighted score matching estimator to point processes. Theoretically, we prove the consistency of our estimator and establish its rate of convergence. Experimental results indicate that our estimator accurately estimates model parameters on synthetic data and yields results consistent with MLE on real data. In contrast, existing score matching estimators fail to perform effectively. Codes are publicly available at https://***/KenCao2007/WSM_TPP.

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GA-Net: Gated Attention Mechanism Based Global Refinement Network for Image Inpainting 29

GA-Net: Gated Attention Mechanism Based Global Refinement Ne...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Qiu, Ran Zhao, Shengrong Liang, Hu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

Underwater images are often affected by problems such as light attenuation, color distortion, noise and scattering, resulting in image defects. A novel image inpainting method is proposed to intelligently predict and fill damaged areas for complete and continuous visualization of the image. First, in order to effectively solve the problem of color distortion caused by light refraction in underwater environments, the improved gated attention mechanism is used. This mechanism improves the local details by learning and weighting the important features of the image. Second, gated convolution automatically determines the degree of restoration for each pixel based on local features of the original image. It eliminates distractions such as low contrast and scattering, retaining more original detailed information. By doing so, image inpainting techniques improve the quality and visualization of underwater images. © 2023 IEEE.

关键词： Gated convolution Image inpainting Underwater images

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