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4th International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2024

作者： Khilenko, Volodymyr Zinchenko, Andrii Stepanov, Oleksii Kotuliak, Ivan Ries, Michal Institute of Computer Engineering and Applied Informatics Slovak University of Technology in Bratislava Faculty of Informatics and Information Technologies Bratislava Slovakia Institute of Transport Systems and Technologies National Academy of Sciences of Ukraine Dnipro Ukraine National University of Life and Environmental Sciences of Ukraine Kyiv Department of Information Technologies Ukraine Slovak University of Technology in Bratislava Faculty of Informatics and Information Technologies Bratislava Slovakia

ISBN: (纸本)9798350391183

The theses is in line with works aimed at studying the possibility of using mathematical algorithms for parallel processing of information in reverse blockchain technology. The paper examines the use of parallel signal processing algorithms to improve the speed and quality of control systems for autonomous dynamic objects using reverse blockchain technology. The use of reverse blockchain technology is due to the possibility of loss of control signals and, at the same time, the inadmissibility of loss of control in the event of changes in the conditions and parameters of the operation of the obj ect. The purpose of the work is to analyze the implementation of solutions for parallel information processing for various models of industrially produced equipment and to develop solutions to increase the efficiency of reverse blockchain technology when using parallel data processing. © 2024 IEEE.

关键词： blockchain dynamic objects and technological processes improving the quality of control systems instability of communication channels loss of information and control signals reverse blockchain technology

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REX: A Realistic Time-Dependent Model for Multimodal Public Transport 22

REX: A Realistic Time-Dependent Model for Multimodal Public ...

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22nd Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems, ATMOS 2022

作者： Kontogiannis, Spyros Machaira, Paraskevi-Maria-Malevi Paraskevopoulos, Andreas Zaroliagis, Christos Computer Science & Engineering Department University of Ioannina Greece Computer Technology Institute & Press "Diophantus" Rion Greece Department of Computer Engineering & Informatics University of Patras Greece

ISBN: (纸本)9783959772594

We present the non-FIFO time-dependent graph model with REalistic vehicle eXchange times (REX) for schedule-based multimodal public transport, along with a novel query algorithm called TRIP-based LAbel-correction propagation (TRIPLA) algorithm that efficiently solves the realistic earliest-arrival routing problem. The REX model possesses all strong features of previous time-dependent graph models without suffering from their deficiencies. It handles non-negligible exchanges from one vehicle to another, as well as supports non-FIFO instances which are typical in public transport, without compromising space efficiency. We conduct a thorough experimental evaluation with real-world data which demonstrates that TRIPLA significantly outperforms all state-of-the-art query algorithms for multimodal earliest-arrival routing in schedule-based public transport. © Spyros Kontogiannis, Paraskevi-Maria-Malevi Machaira, Andreas Paraskevopoulos, and Christos Zaroliagis.

关键词： Graph theory

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Wild Gibbon Optimization Algorithm

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computers, Materials & Continua 2024年第7期80卷 1203-1233页

作者： Jia Guo JinWang Ke Yan Qiankun Zuo Ruiheng Li Zhou He Dong Wang Yuji Sato Hubei Key Laboratory of Digital Finance Innovation Hubei University of EconomicsWuhan430205China School of Information Engineering Hubei University of EconomicsWuhan430205China China Construction Third Engineering Bureau Installation Engineering Co. Ltd.Wuhan430074China Hubei Internet Finance Information Engineering Technology Research Center Hubei University of EconomicsWuhan430205China College of Informatics Huazhong Agricultural UniversityWuhan430070China Faculty of Computer and Information Sciences Hosei UniversityTokyo184-8584Japan

Complex optimization problems hold broad significance across numerous fields and ***,as the dimensionality of such problems increases,issues like the curse of dimensionality and local optima trapping also *** address these challenges,this paper proposes a novel Wild Gibbon Optimization Algorithm(WGOA)based on an analysis of wild gibbon population *** two strategies:community search and community *** community search strategy facilitates information exchange between two gibbon families,generating multiple candidate solutions to enhance algorithm ***,the community competition strategy reselects leaders for the population after each iteration,thus enhancing algorithm *** assess the algorithm’s performance,CEC2017 and CEC2022 are chosen as test *** the CEC2017 test suite,WGOA secures first place in 10 *** the CEC2022 benchmark functions,WGOA obtained the first rank in 5 *** ultimate experimental findings demonstrate that theWildGibbonOptimization Algorithm outperforms others in tested *** underscores the strong robustness and stability of the gibbonalgorithm in tackling complex single-objective optimization problems.

关键词： Complex optimization wild gibbon optimization algorithm community search community competition

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IoT Service Composition - An Estimation of Distribution Algorithm with Adaptive Bias

IoT Service Composition - An Estimation of Distribution Algo...

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2023 IEEE Congress on Evolutionary Computation, CEC 2023

作者： Sun, Fengyang Ma, Hui Chen, Gang Hartmann, Sven School of Engineering and Computer Science Victoria University of Wellington Wellington6012 New Zealand Clausthal University of Technology Department of Informatics Clausthal-Zellerfeld38678 Germany

ISBN: (纸本)9798350314588

Service composition in Internet of Things (SCIoT), as an emerging topic in service computing, aims to select optimal services to complete user requests according to various user requirements such as minimizing energy consumption and response time. To solve this NP-hard problem, numerous heuristic methods, e.g., local search and population-based algorithms, have been proposed, wherein Estimation of Distribution Algorithm (EDA) gains increasing attention because of its explicit global probabilistic nature. However, existing EDAs increase solution diversity by using fixed bias, yet interfere the stability of the learned distribution in the later stage of optimization. Therefore, this paper proposes an EDA with an adaptive bias strategy (EDA-AdaBias) to solve the service composition in IoT problem. The decreasing bias value is added onto the probability values for all choices of each solution variable over generations, which improves diversity of sampled solutions and avoids dramatic change of constructed distribution. Experiments indicate that EDA-AdaBias presents promising performance compared to other competitive methods on this problem. © 2023 IEEE.

关键词： Heuristic methods

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Online Resource Allocation for User Experience Improvement in Heterogeneous MEC Systems

Online Resource Allocation for User Experience Improvement i...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Ni, Weiya Tong, Jingwen Fu, Liqun Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Xiamen University Institute of Artificial Intelligence Xiamen361005 China Xiamen University School of Informatics Xiamen361005 China

ISBN: (纸本)9798350351255

Mobile edge cloud (MEC) has emerged as a critical technology for enabling low-latency and real-time mobile device applications. However, an efficient resource allocation framework for improving the user experience in MEC with heterogeneous users is still missing, especially considering the recent sparks of AI-generated content applications. This paper proposes a double-closed-loop online resource allocation (DORA) framework for user experience improvement. This framework employs inner and outer loops to construct the optimal online allocation strategy and recommend a suitable strategy for different types of users, respectively. Based on the DORA framework, we put forth OR2A-HetU, an Online Resource Recommendation and Allocation algorithm for Heterogeneous Users, to solve this resource allocation problem. The OR2A-HetU algorithm proceeds sequentially and can converge to the optimal solution when the time horizon is sufficiently large. The numerical results show that the proposed algorithm outperforms the baseline algorithms, and the user complaint rate decreases from 48.8% to 27.5% when the available resources increase. © 2024 IEEE.

关键词： Mobile edge computing

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A multi-stage review framework for AI-driven predictive maintenance and fault diagnosis in photovoltaic systems

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Applied Energy 2025年 393卷

作者： Hamza, Ali Ali, Zunaib Dudley, Sandra Saleem, Komal Uneeb, Muhammad Christofides, Nicholas London United Kingdom London United Kingdom Islamabad Pakistan Department of Electrical Engineering Computer Engineering and Informatics Frederick University Nicosia Cyprus

The photovoltaic (PV) sector encounters challenges such as high initial costs, reliance on weather, susceptibility to faults, irregularities in the grid, and degradation of components. Predictive maintenance (PdM) aims to proactively identify issues, thereby enhancing reliability and efficiency but may lack specific fault details without additional diagnostic efforts. This research presents an advanced PdM and fault diagnosis framework that integrates fault pattern analysis, severity assessments, and critical fault predictions. It aims to improve the functionality of PV systems, minimize downtime, and enhance reliability by identifying and analyzing specific fault patterns. Consequently, our article provides a critical review of current Artificial Intelligence (AI) methodologies for PdM and fault diagnosis in PV systems. Moreover, this study highlights the significance of data standardization and offers recommendations on how PdM, when combined with fault diagnosis, can utilize various data sources to anticipate faults in advance, assess their severity, and optimize system performance and maintenance activities. To the best of the authors’ knowledge, no such review study exists. © 2025

关键词： Predictive maintenance

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Interdisciplinary perspectives on privacy awareness in lifelogging technology development

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Journal of Ambient Intelligence and Humanized Computing 2023年第3期14卷 2291-2312页

作者： Wilkowska, Wiktoria Offermann, Julia Colonna, Liane Florez-Revuelta, Francisco Climent-Pérez, Pau Mihailidis, Alex Poli, Angelica Spinsante, Susanna Ziefle, Martina Human-Computer Interaction Center RWTH Aachen University Aachen Germany Swedish Law and Informatics Research Institute Stockholm University Stockholm Sweden Department of Computer Technology University of Alicante Alicante Spain Department of Occupational Science and Occupational Therapy University of Toronto Toronto Canada Department of Information Engineering Marche Polytechnic University Ancona Italy

Population aging resulting from demographic changes requires some challenging decisions and necessary steps to be taken by different stakeholders to manage current and future demand for assistance and support. The consequences of population aging can be mitigated to some extent by assisting technologies that can support the autonomous living of older individuals and persons in need of care in their private environments as long as possible. A variety of technical solutions are already available on the market, but privacy protection is a serious, often neglected, issue when using such (assisting) technology. Thus, privacy needs to be thoroughly taken under consideration in this context. In a three-year project PAAL (‘Privacy-Aware and Acceptable Lifelogging Services for Older and Frail People’), researchers from different disciplines, such as law, rehabilitation, human-computer interaction, and computer science, investigated the phenomenon of privacy when using assistive lifelogging technologies. In concrete terms, the concept of Privacy by Design was realized using two exemplary lifelogging applications in private and professional environments. A user-centered empirical approach was applied to the lifelogging technologies, investigating the perceptions and attitudes of (older) users with different health-related and biographical profiles. The knowledge gained through the interdisciplinary collaboration can improve the implementation and optimization of assistive applications. In this paper, partners of the PAAL project present insights gained from their cross-national, interdisciplinary work regarding privacy-aware and acceptable lifelogging technologies. © 2022, The Author(s).

关键词： Human computer interaction

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A Social Community Sensor for Natural Disaster Monitoring in Indonesia Using Hybrid 2D CNN LSTM 23

A Social Community Sensor for Natural Disaster Monitoring in...

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8th International Conference on Sustainable Information engineering and technology, SIET 2023

作者： Faisal, Mohammad Reza Nugrahadi, Dodon Turianto Budiman, Irwan Muliadi Delimayanti, Mera Kartika Prastya, Septyan Eka Khairie, Muhammad Lambung Mangkurat University Computer Science Department Indonesia Politeknik Negeri Jakarta Computer and Informatics Engineering Department Indonesia Sari Mulia University Department of Information Technology Indonesia Computer Science Department Universitas Mangkurat University Indonesia

ISBN: (纸本)9798400708503

One of the most notable advantages of utilizing social media is its ability to aid communication during natural disasters, specifically in regards to disseminating information. Social media users serve as community monitors by collecting public messages from a multitude of social media platforms. The implementation of artificial intelligence can facilitate the automatic recognition of messages concerning natural disasters. Deep learning-based artificial intelligence models for text classification, such as Convolutional Neural Network (CNN) and Long Short-Term Memory networks (LSTM), each possess their own unique strengths and weaknesses. Nevertheless, the utilization of word padding techniques presents a further challenge to accurately classify texts, as its implementation may negatively impact classification performance. In this study, feature extraction based on word embedding and word padding techniques were employed, utilizing a maximum number of words to be processed by a hybrid 2D CNN LSTM model. The results indicate the highest level of accuracy in cases of floods, with an 81.27% accuracy rate, forest fires with an 86.14% accuracy rate, and earthquakes with an 80.16% accuracy rate. This outcome represents a significant advancement over the accuracy attained by the classification model constructed with feature extraction based on word embedding and word padding based on mean and using solely 2D CNN. © 2023 ACM.

关键词： Long short-term memory

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Smart vegetable cutter for Smart Home 1

Smart vegetable cutter for Smart Home

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Singhal, Saurabh Mangal, Deepak Kumar, Rakesh Sharma, Bhisham Dhaou, Imed Ben GLA University Department of Computer Engineering & Applications Mathura India Jain University Department of Computer Science and IT Bangalore India Dar Al-Hekma University Hekma School of Engineering Computing and Informatics Department of Computer Science Jeddah Saudi Arabia Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9781665472753

Modern society strives for simpler, more efficient, dependable, speedier, and more automated lifestyle. Existing vegetable cutters in the market have drawbacks such as high investment costs, additional manpower, and time consumption. Moreover, none of them provide the feature of cleaning, peeling, and cutting in a single machine. The veggies offered in the market have pesticides and chemicals, and many people, in their haste, do not wash them properly, endangering their health. To reduce human labor while also providing good health care, a solution to this problem is required. This paper offers a novel method for developing a vegetable cutter and chopper using an Android application and IoT devices. In addition, smart vegetable cutters clean, peel, and cut veggies all in one piece of equipment. This strategy will assist human society in reducing the amount of additional manpower needed and would save them time. © 2023 IEEE.

关键词： Real time systems

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SACR: Self-training with Saliency-Augmented Consistency Regularization for Few-Shot Learners

SACR: Self-training with Saliency-Augmented Consistency Regu...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Feng, Yanyan Zhou, Yue Xue, Yun Li, Fenghuan Lin, Zehong School of Electronics and Information Engineering South China Normal University Foshan China Department of Artiffcial Intelligence School of Informatics Xiamen University Xiamen China School of Computer Science and Technology Guangdong University of Technology Guangzhou China

ISBN: (纸本)9798350368741

Pre-trained language models have made significant strides in natural language processing tasks, enabling flexible fine-tuning for downstream applications. However, in few-shot learning scenarios, pre-trained models face challenges related to overfitting due to limited training samples, which hinders their ability to capture data diversity and robustly handle input variations. To overcome these limitations, we propose the Saliency-Augmented Consistency Regularization (SACR) framework, a novel self-training strategy designed to improve few-shot learning performance and robustness. SACR consists of three key components: (1) Saliency-guided Data Perturbation, which uses saliency analysis to identify and perturb words that significantly influence model predictions, generating semantically consistent pseudo-samples;(2) Semantic-equivalent Sample Mining, which employs K-means clustering to select semantically similar samples and prevent semantic shift;and (3) Consistency Training, where regularization ensures consistency between the semantics and prediction distributions of the original and perturbed samples. We conducted extensive experiments on 15 public datasets to evaluate our approach. SACR significantly outperforms strong baseline models and demonstrates superior generalization capabilities compared to state-of-the-art methods, achieving an average 1.0% improvement in classification accuracy. © 2025 IEEE.

关键词： consistency regularization few-shot learning saliency self-training

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