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7th International Conference on Networking, Intelligent systems and Security, NISS 2024

作者： Oubaalla, Abdelwahid Elmoubtahij Merras Elakkad, Nabil Engineering Systems and Applications Laboratory Ensa Sidi Mohamed Ben Abdellah University Fez Morocco Computer Engineering Higher School of Technology University of Ibn Zohr Agadir Morocco Image Laboratory High School of Technology Moulay Ismail University Meknes Morocco

ISBN: (纸本)9798400709296

Colon and lung cancers are responsible for the deaths of thousands of people worldwide every year. Early detection of these cancers can significantly increase the chances of recovery. This is achieved by analyzing histopathological images. This task has traditionally been performed by manual methods. As a result, the results of these analysis are highly dependent on the skills and professional experience of the pathologist, consuming time and manpower. Several studies have attempted to automate this task using deep learning techniques, in particular the convolutional neural network (CNN) models. In this paper, in order to enhance the classification performances on LC25000 histopathological images, we used transfer learning techniques to train three models based on the pre-trained models: VGG, ResNet and DenseNet. Next, we created a model based on the ensemble learning technique in order to enhance the classification performance of these images. This model successfully improved classification performances by achieving an accuracy of 97.4% on test data, 97% on training data and 97.5% on validation data. The aim of this paper is to demonstrate the effect of combining transfer learning and ensemble learning techniques in improving the performances of colon and lung cancer histopathological image classification. © 2024 ACM.

关键词： Lung cancer

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Local Path Planning of Unmanned Surface Vehicle Based on Improved DWA Algorithm

Local Path Planning of Unmanned Surface Vehicle Based on Imp...

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International Conference on Guidance, Navigation and control, ICGNC 2024

作者： Ji, Chenjia Zhang, Lanyong College of Intelligent Systems Science and Engineering Harbin Engineering University Harbin150001 China State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin150001 China

ISBN: (纸本)9789819622351

An improved dynamic window method is proposed to solve the obstacle avoidance problem of intelligent ships in offshore waters that cannot effectively avoid when encountering unknown dynamic obstacles. Firstly, an autonomous collision avoidance decision-making model was constructed to judge whether the unmanned boat had a collision hazard, and the encounter situation was reclassified. To ensure that the USV can safely pass through the moving obstacle vessel, a collision risk assessment function considering the velocity of dynamic obstacles is introduced. Moreover, a collision avoidance rule function is incorporated into the evaluation function of the collision avoidance algorithm to ensure that the USV adheres to collision avoidance regulations. Ultimately, the simulation results confirm that the enhanced dynamic window method can safely avoid collisions while adhering to collision avoidance rules. This paper proposes an effective solution for collision avoidance behavior of intelligent vessels in near shore waters when facing unknown dynamic obstacles, providing insightful recommendations for subsequent research in maritime sectors. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Unmanned surface vehicles

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On-the-fly Surrogation for Complex Nonlinear Dynamics

arXiv

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arXiv 2025年

作者： Olucha, E. Javier Singh, Rajiv Das, Amritam Tóth, Roland Control Systems Group Eindhoven University of Technology Netherlands The MathWorks Inc. Natick United States Systems and Control Laboratory HUN-REN Institute for Computer Science and Control Hungary

High-fidelity models are essential for accurately capturing nonlinear system dynamics. However, simulation of these models is often computationally too expensive and, due to their complexity, they are not directly suitable for analysis, control design or real-time applications. Surrogate modelling techniques seek to construct simplified representations of these systems with minimal complexity, but adequate information on the dynamics given a simulation, analysis or synthesis objective at hand. Despite the widespread availability of system linearizations and the growing computational potential of autograd methods, there is no established approach that systematically exploits them to capture the underlying global nonlinear dynamics. This work proposes a novel surrogate modelling approach that can efficiently build a global representation of the dynamics on-the-fly from local system linearizations without ever explicitly computing a model. Using radial basis function interpolation and the second fundamental theorem of calculus, the surrogate model is only computed at its evaluation, enabling rapid computation for simulation and analysis and seamless incorporation of new linearization data. The efficiency and modelling capabilities of the method are demonstrated on simulation examples. © 2025, CC BY.

关键词： Differentiation (calculus)

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Adaptive PID and Fuzzy Logic controller with Expert System for Longitudinal Movement in Adaptive Cruise control 5th

Adaptive PID and Fuzzy Logic Controller with Expert System f...

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5th International Conference on Advanced technologies and Humanity, ICATH 2023

作者： El Koudia, Younes Jarou, Tarik bakir, Mohamed Abdouni, Jawad Nasri, Elmahdi Advanced Systems Engineering Laboratory Ibn Tofail University Kenitra Morocco Engineering Industrial Management and Innovation Laboratory Hassan First University Settat Morocco

ISBN: (纸本)9783031709913

The purpose of this work is to create an expert system tailored to rectify inaccuracies in control input estimation that are frequently caused by improper gain values or rule-based designs that aren't compatible with the dynamics of the system. PID controllers and fuzzy logic controllers are a common practice in many different control systems. The main goal of this research is to make control inputs more adaptable by incorporating an expert system that can handle problems with PID and fuzzy logic controller modifications. The methodology involves creating an expert system capable of identifying internal error signals and dynamically adjusting gains to enhance the primary controller's control input estimation. To assess the system's effectiveness in controlling PID and fuzzy logic controllers, particularly in the context of longitudinal movement, rigorous experiments were carried out using input signals surpassing the saturation limit specified in the original design. Through a comprehensive assessment that encompassed diverse variable input values and saturation limits, the study consistently demonstrated the superiority of the adaptive PID controller, it outperformed in terms of %OS and settling time values, especially when it came to the regulation of longitudinal movement. This study emphasizes the critical part that expert systems play in improving control input estimates within the primary controller and so increasing system efficiency beyond that of the initial setup, in order to accelerates the development of intelligent control systems and boosting overall performance, with practical applications in longitudinal movement control, by combining expert systems with PID and fuzzy logic controllers. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Expert systems

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Traffic control, Congestion Management and Smart Parking through VANET, ML, and IoT: A Review 10

Traffic Control, Congestion Management and Smart Parking thr...

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10th International Conference on Wireless Networks and Mobile Communications, WINCOM 2023

作者： Ouhmidou, Hajar Nabou, Abdellah Ikidid, Abdelouafi Bouassaba, Wafa Ouzzif, Mohammed El Kiram, Moulay Ahmed Cadi Ayyad University Computing Systems Enginnering Laboratory Computer Sciences Dept Marrakesh Morocco Est Ensem Hassan Ii University of Casablanca Computer Science and Smart Systems Laboratory Casablanca Morocco

ISBN: (纸本)9798350329674

As the quantity of cars increases on the road, so does the focus on improving road safety and in-vehicle entertainment. In response to this need, the creation of new in-car applications and services is on the rise. Common applications include collision avoidance, safety and traffic monitoring, multimedia streaming, and smart city data collection using wireless sensor networks. The Internet of Things (IOT) is a recent innovation that is accelerating the universe towards automated procedures and intelligent transportation management solutions. This is an important contribution to automation and intelligent civilization. Another way to improve the transport system is through the machine learning (ML). an Existing traffic management solution face a number of problems, resulting in traffic congestion, delays and high fatality rates. In addition, vehicular ad hoc network (VANET) has emerged as a new technology capable of supporting such emerging vehicular applications. This paper presents a comprehensive review of the latest state of the traffic control, traffic management and parking and smart city strategy of the vehicle by using VANET, ML and Internet of Things technology. © 2023 IEEE.

关键词： Internet of things

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Network Embedding Methods: Study and Comparison

EEA - Electrotehnica, Electronica, Automatica

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EEA - Electrotehnica, Electronica, Automatica 2024年第4期72卷 72-78页

作者： Benbatata, Sabrina Saoud, Bilal LIMPAF laboratory Department of Computer Sciences University of Bouira Bouira10000 Algeria Electrical Engineering Department Sciences and Applied Sciences Faculty LISEA laboratory University of Bouira Bouira10000 Algeria

Graph is a powerful language that can model many systems in different fields such as information sciences, social sciences, Biology, mathematics, physics, etc. Graphs can capture very well the relationships between nodes and their structure. Representing data through graphs has some limitations and is challenging to use them like input in machine learning and deep learning models. This challenge can be overcome by using network embedding. Embedding represents a network into low-dimensional vector space. Several methods have been proposed to embed networks. Methods like DeepWalk, Node2Vec and GraphSAGE have gained traction. DeepWalk employs random walks to capture local neighborhood information, generating embeddings using skip-gram models. Node2Vec extends this by incorporating biased random walks, balancing exploration and exploitation of the network. GraphSAGE adopts a neighborhood aggregation strategy, aggregating features from a node's local graph structure through different convolutional layers. These methods facilitate downstream tasks like node classification, link prediction and clustering. Challenges include scalability to large networks, handling heterogeneous information and preserving structural characteristics. Recent advancements integrate techniques like attention mechanisms and reinforcement learning for improved embeddings. Network embedding methods continue to evolve, catering to diverse applications in social networks, biology and recommendation systems, offering insights into complex network structures. In this paper we provide networks embedding concepts and some methods. A comparison between embedding methods is presented. Embedding network methods on node classification tasks on different benchmark datasets have been conducted in order to evaluate them. Through rigorous evaluation, we assess their effectiveness in capturing latent features and improving classification accuracy across various domains. © Editura ELECTRA 2024. All rights reserved

关键词： Graph embeddings

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Deep Reinforcement Learning control of Type-1 Diabetes with Cross-Patient Generalization 32

Deep Reinforcement Learning Control of Type-1 Diabetes with ...

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32nd Mediterranean Conference on control and Automation, MED 2024

作者： Atanasious, Mohab M. H. Becchetti, Valentina Baldisseri, Federico Menegatti, Danilo Wrona, Andrea Sapienza University of Rome Control and Management Engineering 'Antonio Ruberti' Department of Computer Rome00185 Italy

ISBN: (纸本)9798350395440

Type 1 diabetes is one of the major concerns in current medical studies, as the World Health Organisation plans to reduce mortality due to such disease by one third by 2030. Standard clinical practice involves self-administered injections of insulin, while current research in the field of automatic control of blood glucose concentration mostly focuses on model-based control techniques. This work presents an application of a Deep Reinforcement Learning-based controller for autonomous treatment of type 1 diabetes, building on the Deep Determin-istic Policy Gradient algorithm. Such control framework is applied for the first time on the Python implementation of the UVA/Padova simulator, named Simglucose. The proposed methodology is validated through in-vitro simulations on an inter-cluster cross-generalization group of virtual adult patients, showing that normoglycemia is successfully preserved while assuring cross-patient generalization and outperforming clinical practice, without the direct knowledge of the amount of ingested carbohydrates. © 2024 IEEE.

关键词： Intelligent systems

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Constraint-Feature-Guided Evolutionary Algorithms for Multi-Objective Multi-Stage Weapon-Target Assignment Problems

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Journal of systems Science & Complexity 2025年第3期38卷 972-999页

作者： WANG Danjing XIN Bin WANG Yipeng ZHANG Jia DENG Fang WANG Xianpeng School of Automation Beijing Institute of Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University

The allocation of heterogeneous battlefield resources is crucial in Command and control(C2). Balancing multiple competing objectives under complex constraints so as to provide decisionmakers with diverse feasible candidate decision schemes remains an urgent challenge. Based on these requirements, a constrained multi-objective multi-stage weapon-target assignment(CMOMWTA) model is established in this paper. To solve this problem, three constraint-feature-guided multi-objective evolutionary algorithms(CFG-MOEAs) are proposed under three typical multi-objective evolutionary frameworks(i.e., NSGA-Ⅱ, NSGA-Ⅲ, and MOEA/D) to obtain various high-quality candidate decision schemes. Firstly, a constraint-feature-guided reproduction strategy incorporating crossover,mutation, and repair is developed to handle complex constraints. It extracts common row and column features from different linear constraints to generate the feasible offspring population. Then, a variable-length integer encoding method is adopted to concisely denote the decision schemes. Moreover,a hybrid initialization method incorporating both heuristic methods and random sampling is designed to better guide the population. Systemic experiments are conducted on three CFG-MOEAs to verify their effectiveness. The superior algorithm CFG-NSGA-Ⅱ among three CFG-MOEAs is compared with two state-of-the-art CMOMWTA algorithms, and extensive experimental results demonstrate the effectiveness and superiority of CFG-NSGA-Ⅱ.

关键词： constrained multi-objective optimization problem constraint handling Evolutionary algorithms weapon-target assignment

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Online Learning-Based Offloading Decision and Resource Allocation in Mobile Edge Computing-Enabled Satellite-Terrestrial Networks

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China Communications 2024年第3期21卷 230-246页

作者： Tong Minglei Li Song Han Wanjiang Wang Xiaoxiang School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China Key Laboratory of Universal Wireless Communications Ministry of EducationBeijing University of Posts and TelecommunicationsBeijing 100876China School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing 100876China

Mobile edge computing(MEC)-enabled satellite-terrestrial networks(STNs)can provide Internet of Things(IoT)devices with global computing ***,the network state information is uncertain or *** deal with this situation,we investigate online learning-based offloading decision and resource allocation in MEC-enabled STNs in this *** problem of minimizing the average sum task completion delay of all IoT devices over all time periods is *** decompose this optimization problem into a task offloading decision problem and a computing resource allocation problem.A joint optimization scheme of offloading decision and resource allocation is then proposed,which consists of a task offloading decision algorithm based on the devices cooperation aided upper confidence bound(UCB)algorithm and a computing resource allocation algorithm based on the Lagrange multiplier *** results validate that the proposed scheme performs better than other baseline schemes.

关键词： computing resource allocation mobile edge computing satellite-terrestrial networks task offloading decision

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Semi-Global Robust Practical Output Voltage Tracking for Single-Phase UPS Inverters via Sampled-Data control 42

Semi-Global Robust Practical Output Voltage Tracking for Sin...

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42nd Chinese control Conference, CCC 2023

作者： Zeng, Kaiji Liu, Wei Sun, Weijie Liu, Weidong Ministry of Education School of Automation Science and Engineering South China University of Technology Key Laboratory of Autonomous Systems and Networked Control Guangzhou510641 China

ISBN: (纸本)9789887581543

This paper deals with the sampled-data semi-global robust practical output voltage tracking problem for single-phase voltage source uninterruptible power supply inverters. We construct an aperiodic sampled-data state feedback control law in an internal model principle framework. Lyapunov analysis shows that as long as the upper bound of the sampling time interval of the controller is less than an explicitly computable threshold, our problem can be handled by the proposed sampled-data control law. And the tracking error is guaranteed to approach a given neighborhood of the origin asympotically. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Uninterruptible power systems

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