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Anticipating the lifespan: Predicting the durability of an anode-supported solid oxide fuel cell short stack over 50,000 h

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Progress in Natural science:Materials International 2024年第3期34卷 606-613页

作者： Muhammad Zubair Khan Amjad Hussain Seung-Bok Lee Tak-Hyoung Lim Rak-Hyun Song Department of Materials Science & Engineering Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology Hydrogen Energy Research Division Korea Institute of Energy Research Department of Advanced Energy and System Engineering Korea University of Science and Technology (UST)

In the present study, the operational lifetime of a solid oxide fuel(SOFC) short stack is predicted by investigating the performance degradation of both the short stack and its cells throughout 1000 h at 800°C. The short stack and integral cell voltages are continuously measured during the long-term test, with electrochemical impedance spectroscopy(EIS) conducted every 200 h. The short stack voltage decreased rapidly for the initial 200–300 h and afterwards, it decreased at a slow rate due to the increase in the Ohmic and polarization resistances in the same manner. Scanning electron microscopy results show that there is no delamination or cracking among constituent layers of the short-stack cells. The single degradation effects of the Ni coarsening in the anode, cation migration and surface segregation in cathode and oxide scale growth in metallic interconnect mesh are successfully integrated into a comprehensive lifetime prediction model. The experimentally measured voltage degradation data of the short stack fits well with the developed mathematical model and allows the successful prediction of the lifetime up to 50,000 h.

关键词： SOFC Stack Degradation Impedance Lifetime

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Data driven Approaches for Demand Forecasting in Supply Chain for Business Decisions 5

Data driven Approaches for Demand Forecasting in Supply Chai...

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5th International Conference on Advancements in Computational sciences, ICACS 2024

作者： Javed, Salman Akhtar, Rizwan Pak-Austria Fachhochschule Institute of Applied Sciences and Technology Dept. It and Computer Science Haripur Pakistan

ISBN: (纸本)9798350394764

Demand forecasting is viewed as a complex challenge for industries as it relies heavily on various factors. The Supply Chain Management (SCM) integrates processes vital for corporate environments across industries. This paper presents a comprehensive data-driven framework for sales demand forecasting in SCM aiming to enhance accuracy of forecasts. Using Rossmann Stores data case study the sales are predicted up to 6 weeks ahead with a comparative analysis conducted for long-term predictions. Multi-dimensional data complexity is addressed by identifying relevant features and selecting appropriate algorithms. The proposed framework utilizes four data-driven machine learning algorithms: Extreme Learning Machine (ELM), Decision Tree (DT), Random Forests (RF), and Extreme Gradient Boosting (XGBoost) aiming to construct a high-precision sales demand forecasting model. The experimental validation using Rossmann Stores data demonstrates the effectiveness of the proposed framework, highlighting its significance in improving the accuracy of forecasts in SCM and enhancing decision-making processes. © 2024 IEEE.

关键词： Forecasting

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Highly dense and ultrathin gadolinia-doped ceria interlayer for enhanced performance of large-area anode-supported solid oxide fuel cell

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Progress in Natural science:Materials International 2023年第5期33卷 733-741页

作者： Sang Woo Shin Amjad Hussain Muhammad Zubair Khan Dong Woo Joh Tak-Hyung Lim Seung-Bok Lee Jong-Eun Hong Joon Hyung Shim Rak-Hyun Song Hydrogen Energy Research Division Korea Institute of Energy Research Department of Mechanical Engineering Korea University Department of Advanced Energy and System Engineering Korea University of Science and Technology(UST) Department of Materials Science & Engineering Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology

The fabrication of a highly dense and ultrathin Gd doped ceria(GDC) interlayer on a large-area, porous electrolyte-coated planar anode support using vacuum slurry coating is studied for application in solid oxide fuel cells(SOFCs). The electrolyte-coated planar anode support body is fabricated by tape casting and pre-sintering is performed at 1100, 1150, and 1200°C. The GDC interlayer is coated onto the pre-sintered electrolyte-coated anode support using a vacuum slurry coating process with different immersion times(30–60 s). A highly dense and ultrathin GDC interlayer with a thickness of 1.48 μm is produced after co-sintering at 1400°C. The 1200°C pre-sintered electrolyte-coated anode support showed a thin and highly dense GDC interlayer which is attributed to a lower shrinkage difference between the coated GDC interlayer and pre-sintered body. The electrochemical performance of a vacuum slurry-coated GDC interlayer cell is 38 % higher compared to that of the screen-printed GDC-coated cell at 700°C due to the greatly reduced Ohmic and electrode polarization resistances. The considerable improvement in the performance is attributed to the ultrahigh GDC density which has prevented the secondary phase formation between the cathode and electrolyte. This study presents the economical, scalable, and reproducible process for the fabrication of highly robust GDC interlayer for the large-area planar anode-supported SOFCs.

关键词： Solid oxide fuel cell Vacuum slurry coating GDC interlayer Sintering Performance

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From Social Media to Ballot Box:Leveraging Location-Aware Sentiment Analysis for Election Predictions

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Computers, Materials & Continua 2023年第12期77卷 3037-3055页

作者： Asif Khan Nada Boudjellal Huaping Zhang Arshad Ahmad Maqbool Khan School of Computer Science and Technology Beijing Institute of TechnologyBeijing100081China The Faculty of New Information and Communication Technologies University Abdel-Hamid Mehri Constantine 2Constantine25000Algeria Department of IT and Computer Science Pak-Austria Fachhochschule:Institute of Applied Sciences and TechnologyHaripur22620Pakistan

Predicting election outcomes is a crucial undertaking,and various methods are employed for this purpose,such as traditional opinion polling,and social media ***,traditional polling approaches often struggle to capture the intricate nuances of voter sentiment at local levels,resulting in a limited depth of analysis and *** light of this challenge,this study focuses on predicting elections at the state/regional level along with the country level,intending to offer a comprehensive analysis and deeper insights into the electoral *** achieve this,the study introduces the Location-Based Election Prediction Model(LEPM),which utilizes social media data,specifically Twitter,and integrates location-aware sentiment analysis techniques at both the state/region and country *** predicts the support and opposing strength of each political party/*** determine the location of users/voters who have not disclosed their location information in tweets,the model utilizes a Voter Location Detection(VotLocaDetect)approach,which leverages recent tweets/*** sentiment analysis techniques employed in this study include rule-based sentiment analysis,Valence Aware Dictionary and Sentiment Reasoner(VADER)as well as transformers-based sentiment analysis such as Bidirectional Encoder Representations from Transformers(BERT),BERTweet,and Election based BERT(ElecBERT).This study uses the 2020 United States(US)Presidential Election as a case *** applying the LEPM model to the election,the study demonstrates its ability to accurately predict outcomes in forty-one states,achieving an 0.84 accuracy rate at the state ***,at the country level,the LEPM model outperforms traditional polling *** a low Mean Absolute Error(MAE)of 0.87,the model exhibits more precise predictions and serves as a successful alternative to conventional polls and other *** the extensive social media data,the LEPM model provides nuanc

关键词： Sentiment analysis big data machine learning election predictions social media analysis

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Improving Sentiment Analysis in Election-Based Conversations on Twitter with ElecBERT Language Model

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Computers, Materials & Continua 2023年第9期76卷 3345-3361页

作者： Asif Khan Huaping Zhang Nada Boudjellal Arshad Ahmad Maqbool Khan School of Computer Science and Technology Beijing Institute of TechnologyBeijing100081China The Faculty of New Information and Communication Technologies University Abdel-Hamid Mehri Constantine 2Constantine25000Algeria Department of IT and Computer Science Pak-Austria Fachhochschule:Institute of Applied Sciences and TechnologyHaripur22620Pakistan

Sentiment analysis plays a vital role in understanding public opinions and sentiments toward various *** recent years,the rise of social media platforms(SMPs)has provided a rich source of data for analyzing public opinions,particularly in the context of election-related ***,sentiment analysis of electionrelated tweets presents unique challenges due to the complex language used,including figurative expressions,sarcasm,and the spread of *** address these challenges,this paper proposes Election-focused Bidirectional Encoder Representations from Transformers(ElecBERT),a new model for sentiment analysis in the context of election-related ***-related tweets pose unique challenges for sentiment analysis due to their complex language,sarcasm,*** is based on the Bidirectional Encoder Representations from Transformers(BERT)language model and is fine-tuned on two datasets:Election-Related Sentiment-Annotated Tweets(ElecSent)-Multi-Languages,containing 5.31 million labeled tweets in multiple languages,and ElecSent-English,containing 4.75million labeled tweets in *** outperforms othermachine learning models such as Support Vector Machines(SVM),Na飗e Bayes(NB),and eXtreme Gradient Boosting(XGBoost),with an accuracy of 0.9905 and F1-score of 0.9816 on ElecSent-Multi-Languages,and an accuracy of 0.9930 and F1-score of 0.9899 on *** performance of differentmodels was compared using the 2020 United States(US)Presidential Election as a case *** ElecBERT-English and ElecBERT-Multi-Languages models outperformed BERTweet,with the ElecBERT-English model achieving aMean Absolute Error(MAE)of *** paper presents a valuable contribution to sentiment analysis in the context of election-related tweets,with potential applications in political analysis,social media management,and policymaking.

关键词： Sentiment analysis social media election prediction machine learning transformers

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A 4-Port Broadband High-Isolated MIMO Antenna for Wireless Communication

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Progress In Electromagnetics Research C 2024年 142卷 119-130页

作者： Ali, Ayyaz Rasool, Maryam Zahid, Muhammad Zeeshan Rashid, Imran Siddique, Adil Masood Maqsood, Moazzam Bhatti, Farooq Ahmad Department of Electrical Engineering Military College of Signals National University of Sciences and Technology Islamabad Pakistan Department of Electrical and Computer Engineering Pak–Austria Fachhochschule: Institute of Applied Sciences and Technology KPK Haripur Pakistan

This article proposes a versatile Multiple Input Multiple Output (MIMO) antenna designed for contemporary wireless systems spanning frequencies from 3 to 20 GHz. It serves applications such as 5G mobile, WiFi, WiFi-6E, X-band, partial Ku, and K-band. The original single-element antenna evolves into a 4 × 4 MIMO configuration with optimized ground plane modifications for enhanced performance. A decoupling structure achieves over 20 dB isolation between inter-elements. The feeding structure, featuring a gradually changing design connected to the antenna’s radiating structure, achieves wide bandwidth characteristics. This is further improved by a partial ground structure and slots on the radiating element. The lower frequency band of 3 to 7 GHz is attained with a rectangle-shaped radiator, while semi-circular microstrip lines atop the radiator enable the higher frequency bands of 8 to 15.4 and 18.7 to 20 GHz. The slots and ground structure enhance impedance bandwidth, and semicircles improve the radiation pattern. The MIMO antenna demonstrates measured peak gains of 6.23 dBi at 11.5 GHz, maintaining a radiation efficiency exceeding 80%. Validation through metrics like ECC, DG, CCL, and TARC confirms strong agreement between simulated and experimental results, positioning the MIMO antenna as a robust choice for various wireless communication applications. © 2024, Electromagnetics Academy. All rights reserved.

关键词： Radiators

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A Nonparametric Split and Kernel-Merge Clustering Algorithm

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4443-4457页

作者： Khan, Khurram ur Rehman, Atiq Khan, Adnan Naqvi, Syed Rameez Belhaouari, Samir Brahim Bermak, Amine Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology Haripur22620 Pakistan Hamad Bin Khalifa University Division of Information and Computing Technology Doha34110 Qatar Comsats University Islamabad Wah47040 Pakistan

This work proposes a novel split and kernel-merge clustering (S-KMC), a nonparametric clustering algorithm that combines the strengths of hierarchical clustering, partitional clustering, and density-based clustering. It consists of two main phases: splitting and merging. In the splitting phase, a ranking-based operator is used to divide the data into optimal subclusters. In the merging phase, a kernel function estimates the density of these subclusters after projecting them onto a straight line passing through their centers, facilitating the merging operation. S-KMC is fully nonparametric, eliminating the need for prior information about the data. It effectively handles 1) shape diversity, 2) density variability, 3) high dimensionality, 4) outliers, and 5) missing values. The algorithm offers easily tunable hyperparameters, enhancing its applicability to complex problems and robustness against data anomalies. Experimental analysis on 21 benchmark datasets demonstrates the improved performance of S-KMC in terms of cluster accuracy, handling high-dimensional data, and managing data anomalies and outliers. Comprehensive comparisons with state-of-the-art techniques further validate the superior or comparable performance of the proposed S-KMC algorithm. © 2020 IEEE.

关键词： Clustering algorithms

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Sentiment Analysis of Low-Resource Language Literature Using Data Processing and Deep Learning

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Computers, Materials & Continua 2024年第4期79卷 713-733页

作者： Aizaz Ali Maqbool Khan Khalil Khan Rehan Ullah Khan Abdulrahman Aloraini Department of IT and Computer Science Pak-Austria Fachhochschule:Institute of Applied Sciences and TechnologyHaripur22620Pakistan Software Competence Center Hagenberg Softwarepark 32aHagenberg4232Austria Department of Computer Science School of Engineering and Digital SciencesNazarbayev UniversityAstana010000Kazakhstan Department of Information Technology College of ComputerQassim UniversityP.O.Box 1162BuraydahSaudi Arabia

Sentiment analysis, a crucial task in discerning emotional tones within the text, plays a pivotal role in understandingpublic opinion and user sentiment across diverse *** numerous scholars conduct sentiment analysisin widely spoken languages such as English, Chinese, Arabic, Roman Arabic, and more, we come to grapplingwith resource-poor languages like Urdu literature which becomes a challenge. Urdu is a uniquely crafted language,characterized by a script that amalgamates elements from diverse languages, including Arabic, Parsi, Pashtu,Turkish, Punjabi, Saraiki, and more. As Urdu literature, characterized by distinct character sets and linguisticfeatures, presents an additional hurdle due to the lack of accessible datasets, rendering sentiment analysis aformidable undertaking. The limited availability of resources has fueled increased interest among researchers,prompting a deeper exploration into Urdu sentiment analysis. This research is dedicated to Urdu languagesentiment analysis, employing sophisticated deep learning models on an extensive dataset categorized into fivelabels: Positive, Negative, Neutral, Mixed, and Ambiguous. The primary objective is to discern sentiments andemotions within the Urdu language, despite the absence of well-curated datasets. To tackle this challenge, theinitial step involves the creation of a comprehensive Urdu dataset by aggregating data from various sources such asnewspapers, articles, and socialmedia comments. Subsequent to this data collection, a thorough process of cleaningand preprocessing is implemented to ensure the quality of the data. The study leverages two well-known deeplearningmodels, namely Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), for bothtraining and evaluating sentiment analysis performance. Additionally, the study explores hyperparameter tuning tooptimize the models’ efficacy. Evaluation metrics such as precision, recall, and the F1-score are employed to assessthe effectiv

关键词： Urdu sentiment analysis convolutional neural networks recurrent neural network deep learning natural language processing neural networks

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Consensus MPC-Based hierarchical control of Islanded AC Microgrid 3

Consensus MPC-Based hierarchical control of Islanded AC Micr...

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3rd International Conference on Communication, Computing and Digital Systems, C-CODE 2023

作者： Khan, Zawar Aamir, Muhammad Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology Electrical & Computer Engineering Department Haripur Pakistan

ISBN: (纸本)9798350332391

In this paper, the consensus model predictive control (MPC)-based hierarchical control scheme is presented for the primary and secondary layers of an islanded AC microgrid (MG). The secondary control is designed on the tracker consensus method, in this way a distributed controller in collaboration with the finite time observer will set the referenced values of voltage magnitude and frequency by significantly speeding up the method of synchronous convergence. Furthermore, the primary control has been implemented for each DG separately and comprises Finite Control Set-Model Predictive control (FCS-MPC), and droops control. FCS-MPC regulates PCC reference voltages for each DG unit. Droop Control regulates power exchange within and among DG units. In the proposed hierarchical control approach, information sharing is accomplished by a neighbouring node creating a sparse communication network while fully eliminating the requirement for a centralized controller. The controller effectiveness have been validated against unbalanced load, transients, and load plug-and-play capabilities. The simulation result shows that MG operates effectively using FCS-MPC for parallel inverters of DG units, and the consensus algorithm based distributed secondary controller in MG effectively delivers the anticipated power, voltage, and frequency. © 2023 IEEE.

关键词： Model predictive control

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An enhanced semi-supervised learning method with self-supervised and adaptive threshold for fault detection and classification in urban power grids

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Energy and AI 2024年第3期17卷 65-79页

作者： Jiahao Zhang Lan Cheng Zhile Yang Qinge Xiao Sohail Khan Rui Liang Xinyu Wu Yuanjun Guo Taiyuan University of Technology TaiyuanShanxiChina Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhenGuangdongChina Guangdong Institute of Carbon Neutrality(Shaoguan) ShaoguanGuangdongChina Sino-Pak Center for Artificial Intelligence Pak-Austria Fachhochschule–Institute of Applied Sciences and TechnologyHaripurKhyber PakhtunkhwaPakistan Chengxi Company State Grid Tianjin Electric Power CompanyTianjinChina

With the rapid development of urban power grids and the large-scale integration of renewable energy, traditional power grid fault diagnosis techniques struggle to address the complexities of diagnosing faults in intricate power grid systems. Although artificial intelligence technologies offer new solutions for power grid fault diagnosis, the difficulty in acquiring labeled grid data limits the development of AI technologies in this area. In response to these challenges, this study proposes a semi-supervised learning framework with self-supervised and adaptive threshold (SAT-SSL) for fault detection and classification in power grids. Compared to other methods, our method reduces the dependence on labeling data while maintaining high recognition accuracy. First, we utilize frequency domain analysis on power grid data to filter abnormal events, then classify and label these events based on visual features, to creating a power grid dataset. Subsequently, we employ the Yule–Walker algorithm extract features from the power grid data. Then we construct a semi-supervised learning framework, incorporating self-supervised loss and dynamic threshold to enhance information extraction capabilities and adaptability across different scenarios of the model. Finally, the power grid dataset along with two benchmark datasets are used to validate the model’s functionality. The results indicate that our model achieves a low error rate across various scenarios and different amounts of labels. In power grid dataset, When retaining just 5% of the labels, the error rate is only 6.15%, which proves that this method can achieve accurate grid fault detection and classification with a limited amount of labeled data.

关键词： Power grid fault detection Semi-supervised learning Data driven Deep learning Smart grid

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