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30th Telecommunications Forum, TELFOR 2022

作者： Suvara, Dragana Vajda, Kristina Katanic, Miroslav Kisic, Milica Damnjanovic, Mirjana Babkovic, Kalman Faculty of Technical Sciences University of Novi Sad Department of Computing and Control Engineering Novi Sad Serbia Faculty of Technical Sciences University of Novi Sad Department of Power Electronic and Telecommunication Engineering Novi Sad Serbia

ISBN: (数字)9781665472739

ISBN: (纸本)9781665472739

In this paper a simple capacitive sensor for displacement/force measuring is presented. Additive 3D printing technology was used to make the sensor housing and electrodes, while silicone rubber was used for the dielectric layer to ensure good sensitivity to the applied force. Three different patterns of dielectric layers were investigated. During experimental measurements, a change in capacitance was observed depending on the performed displacement in the range of 0.5 mm with 0.1 mm increments. The measured capacitance values and characteristics for all three types of sensors were compared. © 2022 IEEE.

关键词： Capacitive sensors

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Facial Expression Recognition Model Depending on Optimized Support Vector Machine

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Computers, Materials & Continua 2023年第7期76卷 499-515页

作者： Amel Ali Alhussan Fatma M.Talaat El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Doaa Sami Khafaga Mona Alnaggar Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Machine Learning&Information Retrieval Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Robotics and Intelligent Machines Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt

In computer vision,emotion recognition using facial expression images is considered an important research *** learning advances in recent years have aided in attaining improved results in this *** to recent studies,multiple facial expressions may be included in facial photographs representing a particular type of *** is feasible and useful to convert face photos into collections of visual words and carry out global expression *** main contribution of this paper is to propose a facial expression recognitionmodel(FERM)depending on an optimized Support Vector Machine(SVM).To test the performance of the proposed model(FERM),AffectNet is *** uses 1250 emotion-related keywords in six different languages to search three major search engines and get over 1,000,000 facial photos *** FERM is composed of three main phases:(i)the Data preparation phase,(ii)Applying grid search for optimization,and(iii)the categorization *** discriminant analysis(LDA)is used to categorize the data into eight labels(neutral,happy,sad,surprised,fear,disgust,angry,and contempt).Due to using LDA,the performance of categorization via SVM has been obviously *** search is used to find the optimal values for hyperparameters of SVM(C and gamma).The proposed optimized SVM algorithm has achieved an accuracy of 99%and a 98%F1 score.

关键词： Facial expression recognition machine learning linear dis-criminant analysis(LDA) support vector machine(SVM) grid search

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Al-Biruni Earth Radius Optimization for COVID-19 Forecasting

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Computer Systems Science & engineering 2023年第7期46卷 883-896页

作者： El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Mostafa Abotaleb Tatiana Makarovskikh Amal H.Alharbi Doaa Sami Khafaga Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of System Programming South Ural State University454080ChelyabinskRussia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Several instances of pneumonia with no clear etiology were recorded in Wuhan,China,on December 31,*** world health organization(WHO)called it COVID-19 that stands for“Coronavirus Disease 2019,”which is the second version of the previously known severe acute respiratory syndrome(SARS)Coronavirus and identified in short as(SARSCoV-2).There have been regular restrictions to avoid the infection spread in all countries,including Saudi *** prediction of new cases of infections is crucial for authorities to get ready for early handling of the virus ***:Analysis and forecasting of epidemic patterns in new SARSCoV-2 positive patients are presented in this research using metaheuristic optimization and long short-term memory(LSTM).The optimization method employed for optimizing the parameters of LSTM is Al-Biruni Earth Radius(BER)***:To evaluate the effectiveness of the proposed methodology,a dataset is collected based on the recorded cases in Saudi Arabia between March 7^(th),2020 and July 13^(th),*** addition,six regression models were included in the conducted experiments to show the effectiveness and superiority of the proposed *** achieved results show that the proposed approach could reduce the mean square error(MSE),mean absolute error(MAE),and R^(2)by 5.92%,3.66%,and 39.44%,respectively,when compared with the six base *** the other hand,a statistical analysis is performed to measure the significance of the proposed ***:The achieved results confirm the effectiveness,superiority,and significance of the proposed approach in predicting the infection cases of COVID-19.

关键词： COVID-19 prediction meta-heuristic optimization LSTM Al-Biruni earth radius algorithm

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Towards Long Lifetime Battery:AI-Based Manufacturing and Management

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1139-1165页

作者： Kailong Liu Zhongbao Wei Chenghui Zhang Yunlong Shang Remus Teodorescu Qing-Long Han Warwick Manufacturing Group University of WarwickCoventry CV47ALUnited Kingdom National Engineering Laboratory for Electric Vehicles School of Mechanical EngineeringBeijing Institute of TechnologyBeijing 100081China School of Control Science and Engineering Shandong UniversityJinan 250061China Department of Energy Technology Aalborg UniversityAalborg 9220Denmark School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia IEEE

Technologies that accelerate the delivery of reliable battery-based energy storage will not only contribute to decarbonization such as transportation electrification,smart grid,but also strengthen the battery supply *** battery inevitably ages with time,losing its capacity to store charge and deliver it *** directly affects battery safety and efficiency,making related health management *** advancements in automation science and engineering raised interest in AI-based solutions to prolong battery lifetime from both manufacturing and management *** paper aims at presenting a critical review of the state-of-the-art AI-based manufacturing and management strategies towards long lifetime ***,AI-based battery manufacturing and smart battery to benefit battery health are *** the most adopted AI solutions for battery life diagnostic including state-of-health estimation and ageing prediction are reviewed with a discussion of their advantages and *** through designing suitable AI solutions to enhance battery longevity are also ***,the main challenges involved and potential strategies in this field are *** work will inform insights into the feasible,advanced AI for the health-conscious manufacturing,control and optimization of battery on different technology readiness levels.

关键词： Artificial intelligence battery health management battery life diagnostic battery manufacturing smart battery

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A proposal of Android services generator for inter-connection with ADAS

A proposal of Android services generator for inter-connectio...

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Zooming Innovation in Consumer Electronics International Conference (ZINC)

作者： Ognjen Boskovic Aleksandar Kokinovic Dusan Kenjic Ivan Kastelan Department of Computing and Control Engineering Faculty of Technical Science University of Novi Sad Novi Sad Serbia

The integration of software in the automotive industry has become increasingly complex and time-sensitive, necessitating the implementation of efficient tools. One such tool is the utilization of a generator. Over the past decade, the implementation of In-Vehicle-Infotainment systems has become ubiquitous, occupying a central position within vehicles. Another system present in vehicles is the Advanced Driver Assistance System. Despite their prevalence, these systems operate in isolation. However, the utilization of Android services and a service-client architecture can facilitate communication between disparate systems through the implementation of various service-client-based protocols. This study proposes the development of a generator for Android services that enable communication between Advanced Driver Assistance System systems through the use of CommonAPI proxy. The implementation of this generator will expedite the production process of code that integrates these independent systems.

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One solution of providing ADAS data to the IVI domain via the Android Java service

One solution of providing ADAS data to the IVI domain via th...

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Zooming Innovation in Consumer Electronics International Conference (ZINC)

作者： Dušan Stanišić Marija Antić Dušan Kenjić Luka Bilač Department of Computing and Control Engineering Faculty of Technical Sciences University of Novi Sad Novi Sad Serbia

In recent years, manufacturers are working parallel in developing and integrating two in-vehicle systems, an Advanced Driver Assistance System (ADAS) and In-Vehicle Infotainment (IVI). ADAS’s purpose is to increase safety and aid the driver while the vehicle is in motion, by using various sensors to perceive the vehicle surrounding or the driver’s errors and respond accordingly. Consumer applications in the IVI domain would significantly benefit from accessing such vehicle-specific data. Unfortunately, due to active vehicle system architecture requirements, the IVI domain cannot achieve communication with the ADAS by following traditional signal-oriented mechanisms. In addition, Android paved its way into the IVI domain as the most common Operating System because of its high flexibility and customization, but without being adapted for communication with ADAS. This paper offers a solution for enabling Android applications to access ADAS data via Java services. Data is procured over GENIVI company’s SOME/IP-based communication middleware, integrated within the services. Communication performance is measured, evaluated, and limitations are identified.

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Metaheuristic-Based Lagrangian Relaxation for Total Weighted Tardiness Minimization 21st

Metaheuristic-Based Lagrangian Relaxation for Total Weighte...

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21st Polish control Conference, PCC 2023

作者： Smutnicki, Czeslaw Rudy, Jaroslaw Idzikowski, Radoslaw Bożejko, Wojciech Department of Computing Engineering Wroclaw University of Science and Technology Wybrzeże Wyspiańskiego 27 Wroclaw50-370 Poland Department of Control Systems and Mechatronics Wroclaw University of Science and Technology Wybrzeże Wyspiańskiego 27 Wroclaw50-370 Poland

ISBN: (纸本)9783031351723

In this paper, a Lagrangian relaxation approach to the problem of minimization of the total weighted tardiness is presented. The approach can be used both to solve the original problem as well as to obtain a high-quality lower bound for the branch-and-bound method. The problem was further decomposed into upper and lower problems. For the upper problem, an approach using a local-search metaheuristic is proposed instead of a subgradient method from the literature. Several variants of both methods are then implemented and compared through a numerical example using OR library problem instances. The results prove the viability of the metaheuristic approach as with little calibration it is able to match or outperform a refined subgradient method. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Branch and bound method

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Prediction of piRNA-mRNA interactions based on an interactive inference network

Prediction of piRNA-mRNA interactions based on an interactiv...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Liu, Yajun Li, Ru Li, Aimin Fei, Rong Guo, Xie Wu, Fang-Xiang Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an China University of Saskatchewan Division of Biomedical Engineering Department of Computer Science Department of Mechanical Engineering Saskatoon Canada

ISBN: (纸本)9798350337488

As the largest class of small non-coding RNAs, piRNAs primarily present in the reproductive cells of mammals, which influence post-transcriptional processes of mRNAs in multiple ways. Effective methods for predicting piRNA and mRNA target relationships can help identify piRNA functions, investigate the possibility of piRNAs as biomarkers and therapeutic targets. In this study, we propose a computational approach for classifying the relationships of piRNA-mRNA pairs based on an interactive inference network (IIN). First, we gather piRNA-mRNA target data, collect sequence data by position alignment, and construct a benchmark dataset. Furthermore, a reliable negative set is constructed by positive-unlabeled learning. Finally, we view a piRNA and a mRNA sequence as a premise and hypothesis sentence, respectively, and IIN model is used to predict the relationship between them. The experiments demonstrate that our method effectively characterizes piRNA-mRNA interaction and could be beneficial for researchers to investigate piRNA functions. © 2023 IEEE.

关键词： interactive inference network noncoding RNA piRNA-mRNA interactions positive-unlabeled learning

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DAFNI: A computational platform to support infrastructure systems research

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Proceedings of the Institution of Civil Engineers: Smart Infrastructure and Construction 2023年第3期176卷 108-116页

作者： Matthews, Brian Hall, Jim Batty, Michael Blainey, Simon Cassidy, Nigel Choudhary, Ruchi Coca, Daniel Hallett, Stephen Harou, Julien J James, Phil Lomax, Nik Oliver, Peter Sivakumar, Aruna Tryfonas, Theodoros Varga, Liz Scientific Computing Department Science and Technology Facilities Council Didcot United Kingdom School of Geography and the Environment University of Oxford Oxford United Kingdom Centre for Advanced Spatial Analysis University College London London United Kingdom Transportation Research Group Faculty of Engineering and Physical Sciences University of Southampton Southampton United Kingdom Department of Civil Engineering University of Birmingham Birmingham United Kingdom Department of Engineering University of Cambridge Cambridge United Kingdom Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom Centre for Environmental and Agricultural Informatics Cranfield University Cranfield United Kingdom Department of Mechanical Aerospace and Civil Engineering University of Manchester Manchester United Kingdom School of Engineering Newcastle University Newcastle United Kingdom School of Geography University of Leeds Leeds United Kingdom Department of Civil and Environmental Engineering Imperial College London London United Kingdom Department of Civil Engineering University of Bristol Bristol United Kingdom Department of Civil Environmental and Geomatic Engineering University College London London United Kingdom

Research into the engineering of infrastructure systems is increasingly data intensive. Researchers build computational models to explore scenarios such as investigating the merits of infrastructure plans, analysing historical data to inform system operations or assessing the impacts of infrastructure on the environment. Models are more complex, at higher resolution and with larger coverage. Researchers also require a 'multi-systems' approach to explore interactions between systems, such as energy and water with urban development, and across scales, from buildings and streets to regions or nations. Consequently, researchers need enhanced computational resources to support cross-institutional collaboration and sharing at scale. The Data and Analytics Facility for National Infrastructure (DAFNI) is an emerging computational platform for infrastructure systems research. It provides high-throughput compute resources so larger data sets can be used, with a data repository to upload data and share these with collaborators. Users' models can also be uploaded and executed using modern containerisation techniques, giving platform independence, scaling and sharing. Further, models can be combined into workflows, supporting multi-systems modelling and generating visualisations to present results. DAFNI forms a central resource accessible to all infrastructure systems researchers in the UK, supporting collaboration and providing a legacy, keeping data and models available beyond the lifetime of a project. © 2023 Emerald Publishing Limited: All rights reserved.

关键词： Urban growth

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Interaction-Aware Gaussian Weighting for Clustered Federated Learning

arXiv

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arXiv 2025年

作者： Licciardi, Alessandro Leo, Davide Faní, Eros Caputo, Barbara Ciccone, Marco Department of Mathematical Sciences Polytechnic University of Turin Italy Turin Italy Department of Computing and Control Engineering Polytechnic University of Turin Italy Vector Institute TorontoON Canada

Federated Learning (FL) emerged as a decentralized paradigm to train models while preserving privacy. However, conventional FL struggles with data heterogeneity and class imbalance, which degrade model performance. Clustered FL balances personalization and decentralized training by grouping clients with analogous data distributions, enabling improved accuracy while adhering to privacy constraints. This approach effectively mitigates the adverse impact of heterogeneity in FL. In this work, we propose a novel clustered FL method, FedGWC (Federated Gaussian Weighting Clustering), which groups clients based on their data distribution, allowing training of a more robust and personalized model on the identified clusters. FedGWC identifies homogeneous clusters by transforming individual empirical losses to model client interactions with a Gaussian reward mechanism. Additionally, we introduce the Wasserstein Adjusted Score, a new clustering metric for FL to evaluate cluster cohesion with respect to the individual class distribution. Our experiments on benchmark datasets show that FedGWC outperforms existing FL algorithms in cluster quality and classification accuracy, validating the efficacy of our approach. © 2025, CC BY.

关键词： Federated learning

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