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8th International Conference on Life System Modeling and Simulation, LSMS 2024 and 8th International Conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

作者： Shao, Rui Ben, JiangYuan Lin, Pengcheng Ge, Shu Wu, ChengGang Zhang, Kun He, Ying School of Electrical Engineering and Automation Nantong University Nantong China Department of Ultrasound Tumor Hospital Affiliated to Nantong University Nantong China Nantong Key Laboratory of Intelligent Control and Intelligent Computing Nantong China Nantong Institute of Technology Jiangsu Nantong China

ISBN: (纸本)9789819601875

During thyroid cancer surgery, the debate continues regarding whether to perform routine prophylactic central lymph node dissection on the same side as the lesion. Central lymph node metastasis has been proven to be clinically significant in guiding treatment decisions. Therefore, accurately predicting central lymph node metastasis preoperatively is an significant step in the diagnosis and treatment of thyroid cancer. In clinical practice, ultrasound examination is commonly used to assess thyroid cancer. However, due to the low contrast and high noise of ultrasound images, the accuracy of ultrasound in identifying lymph node metastasis is limited. This study proposes a preoperative prediction of lymph node metastasis based on MFF-Resnet, which is based on Resnet50. This paper constructs multimodal data by combining deep learning with radiomics and clinical factors, and uses auxiliary branches to incorporate original information and low-level features of ultrasound images for prediction tasks. The AUC of MFF-Resnet reaches 0.8576, with an accuracy of 0.8018, and an F1 score of 0.8281. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Diseases

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Fractional calculus in modelling hereditariness and nonlocality in transmission lines 11

Fractional calculus in modelling hereditariness and nonlocal...

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11th International Conference of the Balkan Physical Union, BPU 2022

作者： Zorica, Dušan Cvetićanin, Stevan M. Rapaić, Milan R. Department of Physics Faculty of Sciences University of Novi Sad Trg D. Obradovića 4 Novi Sad Serbia Mathematical Institute Serbian Academy of Arts and Sciences Kneza Mihaila 36 Beograd Serbia Department of Power Electronic and Telecommunication Engineering Faculty of Technical Sciences University of Novi Sad Trg D. Obradovića 6 Novi Sad Serbia Department of Computing and Control Engineering Faculty of Technical Sciences University of Novi Sad Trg D. Obradovića 6 Novi Sad Serbia

Transmission lines are traditionally modelled by considering Heaviside's elementary circuit that contains a resistor and inductor in the series branch, accounting for the energy losses and magnetic effects, while the shunt branch contains a resistor and a capacitor, accounting for the energy losses and capacitive phenomena. Classical telegrapher's equations, modelling the signal propagation in a transmission line, are obtained by assuming the infinitesimal length of the elementary circuit and by passing to a continuum. The generalization of elementary circuit is two-fold: topological by adding the capacitor in the series branch in order to account for the charge accumulation effects along the line and constitutive in order to account for the memory effects that transmission line may display. The constitutive generalization is performed by changing the constitutive relation describing the capacitive and inductive material properties using the fractional calculus approach accounting for the short-tail memory. On the other hand, the inclusion of nonlocal material properties of a transmission line is performed by considering the magnetic coupling of inductors in the series branch of Heaviside's elementary circuit, so that the magnetic flux is obtained as a superposition of local and constitutively given nonlocal magnetic flux through the cross-inductivity kernel. Signal propagation is studied in the case of power, exponential, and Gauss type cross-inductivity kernels. The presented results are published in [1-3]. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).

关键词： Electric lines

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Virtually-Federated Scheduling of Parallel Real-Time Tasks 42

Virtually-Federated Scheduling of Parallel Real-Time Tasks

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42nd IEEE Real-Time Systems Symposium, RTSS 2021

作者： Jiang, Xu Guan, Nan Liang, Haochun Tang, Yue Qiao, Lei Yi, Wang Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University China City University of Hong Kong Hong Kong Beijing Institute of Control Engineering China Uppsala University Sweden

ISBN: (纸本)9781665428026

Federated scheduling is a promising approach to schedule parallel real-time tasks, where each task exclusively executes on a set of dedicated processors. However, federated scheduling suffers significant resource wasting since a task typically only uses part of the processing capacity allocated to it, while the unused part cannot be shared with other tasks. To solve this problem, we present a virtually-federated scheduling approach, which both enjoys the good analyzability of federated scheduling and allows tasks to efficiently share processors with others. The main idea is to construct virtual processors on physical processors, and let a task exclusively execute on a set of virtual processors. As a physical processor is shared by virtual processors, tasks effectively share processors with each other. On the other hand, as each task exclusively executes on its own virtual processor set, the good analyzability of federated scheduling can be carried into to our virtually-federated scheduling approach. We conduct comprehensive performance evaluation to compare our proposed approach with existing methods of different types. Experiment results show that our approach consistently outperforms existing methods to a considerable extent under a wide range of parameter settings. ©2021 IEEE

关键词： Scheduling

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Classification of attention performance post-longitudinal tDCS via functional connectivity and machine learning methods

arXiv

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arXiv 2024年

作者： Rao, Akash K. Menon, Vishnu K. Bhavsar, Arnav Chowdhury, Shubhajit Roy Negi, Ramsingh Dutt, Varun Applied Cognitive Science Laboratory Indian Institute of Technology Mandi Mandi India School of Computing and Electrical Engineering Indian Institute of Technology Mandi Mandi India Cognitive Control and Machine learning group Institute of Nuclear Medicine and Allied Sciences Delhi India

Attention is the brain's mechanism for selectively processing specific stimuli while filtering out irrelevant information. Characterizing changes in attention following long-term interventions (such as transcranial direct current stimulation (tDCS)) has seldom been emphasized in the literature. To classify attention performance post-tDCS, this study uses functional connectivity and machine learning algorithms. Fifty individuals were split into experimental and control conditions. On Day 1, EEG data was obtained as subjects executed an attention task. From Day 2 through Day 8, the experimental group was administered 1mA tDCS, while the control group received sham tDCS. On Day 10, subjects repeated the task mentioned on Day 1. Functional connectivity metrics were used to classify attention performance using various machine learning methods. Results revealed that combining the Adaboost model and recursive feature elimination yielded a classification accuracy of 91.84%. We discuss the implications of our results in developing neurofeedback frameworks to assess attention. Copyright © 2024, The Authors. All rights reserved.

关键词： Electroencephalography

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An approach towards Model-To-Model Mapping between AUTOSAR and Android systems

An approach towards Model-To-Model Mapping between AUTOSAR a...

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Proceedings of the International Convention MIPRO

作者： Radomir Zlatković Vuk Č abrilo Marija Antić Branimir Kovač ević Department of Computing and Control Engineering Faculty of Technical Sciences University of Novi Sad RT-RK Institute for Computer Based Systems Novi Sad Serbia

ISBN: (数字)9789532331035

ISBN: (纸本)9781665484343

Every new generation of modern vehicles aims to provide the customers with a higher level of comfort and entertainment services. Consequently, the burden is placed upon the manufacturers to incorporate ever more complex electronic systems in their products and introduce service-based technologies. To meet these demands, service-oriented architecture (SOA) has become an integral part of the Adaptive AUTOSAR as the industry standard. Also, mobile services and applications are natively supported in the new in-vehicle infotainment systems (IVI) which usually run on the Android operating system. As these fundamentally different technologies are incorporated in the vehicles, the developers are facing the challenge of connecting them and exchanging information between the services running in different domains. Service-oriented architectures usually rely on a model-driven engineering approach to facilitate the development of the applications. However, as the complexity of the system grows, the mapping of various metamodels becomes a challenge, as no single standard is used across all domains in the vehicle. This paper provides a possible solution for mapping the already existing AUTOSAR metamodel to an Android Interface Definition Language (AIDL) metamodel. By applying the proposed mapping, it is possible to build applications that require the cooperation of various domains within the vehicle.

关键词： Operating systems Merging Entertainment industry Model driven engineering Generators Service-oriented architecture Complexity theory

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Metaheuristic Optimization of Time Series Models for Predicting Networks Traffic

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Computers, Materials & Continua 2023年第4期75卷 427-442页

作者： Reem Alkanhel El-Sayed M.El-kenawy D.L.Elsheweikh Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Science Faculty of Specific EducationMansoura UniversityEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia

Traffic prediction of wireless networks attracted many researchersand practitioners during the past decades. However, wireless traffic frequentlyexhibits strong nonlinearities and complicated patterns, which makes it challengingto be predicted accurately. Many of the existing approaches forpredicting wireless network traffic are unable to produce accurate predictionsbecause they lack the ability to describe the dynamic spatial-temporalcorrelations of wireless network traffic data. In this paper, we proposed anovel meta-heuristic optimization approach based on fitness grey wolf anddipper throated optimization algorithms for boosting the prediction accuracyof traffic volume. The proposed algorithm is employed to optimize the hyperparametersof long short-term memory (LSTM) network as an efficient timeseries modeling approach which is widely used in sequence prediction *** prove the superiority of the proposed algorithm, four other optimizationalgorithms were employed to optimize LSTM, and the results were *** evaluation results confirmed the effectiveness of the proposed approachin predicting the traffic of wireless networks accurately. On the other hand,a statistical analysis is performed to emphasize the stability of the proposedapproach.

关键词： Network traffic soft computing LSTM metaheuristic optimization

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Sparse Attention-driven Quality Prediction for Production Process Optimization in Digital Twins

arXiv

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arXiv 2024年

作者： Yin, Yanlei Wang, Lihua Hoang, Dinh Thai Wang, Wenbo Niyato, Dusit The School of Mechanical and Electrical Engineering Kunming University of Science and Technology Kunming650500 China Australia The School of Mechanical and Electrical Engineering Yunnan Key Laboratory of Intelligent Control and Application Kunming University of Science and Technology Kunming650500 China The College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

In the process industry, long-term and efficient optimization of production lines requires real-time monitoring and analysis of operational states to fine-tune production line parameters. However, complexity in operational logic and intricate coupling of production process parameters make it difficult to develop an accurate mathematical model for the entire process, thus hindering the deployment of efficient optimization mechanisms. In view of these difficulties, we propose to deploy a digital twin of the production line by encoding its operational logic in a data-driven approach. By iteratively mapping the real-world data reflecting equipment operation status and product quality indicators in the digital twin, we adopt a quality prediction model for production process based on self-attention-enabled temporal convolutional neural networks. This model enables the data-driven state evolution of the digital twin. The digital twin takes a role of aggregating the information of actual operating conditions and the results of quality-sensitive analysis, which facilitates the optimization of process production with virtual-reality evolution. Leveraging the digital twin as an information-flow carrier, we extract temporal features from key process indicators and establish a production process quality prediction model based on the proposed deep neural network. Our operation experiments on a specific tobacco shredding line demonstrate that the proposed digital twin-based production process optimization method fosters seamless integration between virtual and real production lines. This integration achieves an average operating status prediction accuracy of over 98% and a product quality acceptance rate of over 96%. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Bitroute SOME/IP: Implementation of a Scalable and Service Oriented Communication Middleware

Bitroute SOME/IP: Implementation of a Scalable and Service O...

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Proceedings of the International Convention MIPRO

作者： M. Vujanić N. Trifunović I. Kaš telan B. Kovač ević Department of Computing and Control Engineering Faculty of Technical Sciences University of Novi Sad RT-RK Institute for Computer Based Systems Novi Sad Serbia

ISBN: (数字)9789532331035

ISBN: (纸本)9781665484343

SOME/IP is a communication protocol introduced in automotive industry to enable Service Oriented Architecture which is not possible using traditional automotive protocols such as CAN or FlexRay. A trend emerging in the last years in the Advanced Driver Assistance Systems (ADAS) is that SOME/IP became one of main communication protocols used for communication between safety and performance ECU devices. Currently in the market, including the open-source solutions, there is a set of different SOME/IP implementations that have different audience and combination of characteristics like high royalty costs, available feature set and software solution maturity. This paper presents the Bitroute SOME/IP, a new implementation of the SOME/IP communication protocol with focus on performance and deployment platform independency and compares it with the open-source solution from The Connected Vehicle Systems Alliance (COVESA).

关键词： Performance evaluation Protocols Costs Market research Libraries Safety Service-oriented architecture

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Intelligent Networked control of Vasoactive Drug Infusion for Patients with Uncertain Sensitivity

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Computer Systems Science & engineering 2023年第10期47卷 721-739页

作者： Mohamed Esmail Karar Amged Sayed A.Mahmoud College of Computing and Information Technology(CCIT) Shaqra UniversityShaqra11961Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering(FEE)Menoufia UniversityMenouf32952Egypt Department of Electrical Energy Engineering College of Engineering&TechnologyArab Academy for Science Technology&Maritime TransportP.O.Box 12577Smart VillageSmart Village-B 2401Egypt

Abnormal high blood pressure or hypertension is still the leading risk factor for death and disability *** paper presents a new intelligent networked control of medical drug infusion system to regulate the mean arterial blood pressure for hypertensive patients with different health status *** infusion of vasoactive drugs to patients endures various issues,such as variation of sensitivity and noise,which require effective and powerful systems to ensure robustness and good *** developed intelligent networked system is composed of a hybrid control scheme of interval type-2 fuzzy(IT2F)logic and teaching-learning-based optimization(TLBO)*** networked IT2F control is capable of managing the uncertain sensitivity of the patient to anti-hypertensive drugs *** avoid the manual selection of control parameter values,the TLBO algorithm is mainly used to automatically find the best parameter values of the networked IT2F *** simulation results showed that the optimized networked IT2F achieved a good performance under external disturbances.A comparative study has also been conducted to emphasize the outperformance of the developed controller against traditional PID and type-1 fuzzy ***,the comparative evaluation demonstrated that the performance of the developed networked IT2F controller is superior to other control strategies in previous studies to handle unknown patients’sensitivity to infused vasoactive drugs in a noisy environment.

关键词： Intelligent medical systems telemedicine fuzzy control teaching learning-based optimization

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Dipper Throated Optimization for Detecting Black-Hole Attacks inMANETs

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Computers, Materials & Continua 2023年第1期74卷 1905-1921页

作者： Reem Alkanhel El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Mostafa Abotaleb Doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of System Programming South Ural State UniversityChelyabinsk454080Russia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

In terms of security and privacy,mobile ad-hoc network(MANET)continues to be in demand for additional debate and *** more MANET applications become data-oriented,implementing a secure and reliable data transfer protocol becomes a major concern in the ***,MANET’s lack of infrastructure,unpredictable topology,and restricted resources,as well as the lack of a previously permitted trust relationship among connected nodes,contribute to the attack detection burden.A novel detection approach is presented in this paper to classify passive and active black-hole *** proposed approach is based on the dipper throated optimization(DTO)algorithm,which presents a plausible path out of multiple paths for statistics transmission to boost MANETs’quality of service.A group of selected packet features will then be weighed by the DTO-based multi-layer perceptron(DTO-MLP),and these features are collected from nodes using the Low Energy Adaptive Clustering Hierarchical(LEACH)clustering *** is a powerful classifier and the DTO weight optimization method has a significant impact on improving the classification process by strengthening the weights of key features while suppressing the weights ofminor *** hybridmethod is primarily designed to combat active black-hole *** the LEACH clustering phase,however,can also detect passive black-hole *** effect of mobility variation on detection error and routing overhead is explored and evaluated using the suggested *** diverse mobility situations,the results demonstrate up to 97%detection accuracy and faster execution ***,the suggested approach uses an adjustable threshold value to make a correct conclusion regarding whether a node is malicious or benign.

关键词： Black-hole attack mobile ad-hoc network optimization dipper throated optimization

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