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Development of an Attack Detection Module for Vehicular Ad-hoc Networks

Development of an Attack Detection Module for Vehicular Ad-h...

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International symposium on Applied computational intelligence and Informatics ( SACI)

作者： Rajmund Drenyovszki Zsolt Csaba Johanyák Department of Information Technology John von Neumann University Kecskemét Hungary

ISBN: (数字)9798350329520

ISBN: (纸本)9798350329537

Vehicular ad-hoc networks (VANETs) are rapidly expanding and providing benefits such as smarter traffic control, improved road safety, and increased passenger convenience. However, security remains a significant concern due to the rise in malicious activities aiming to compromise data security and endanger vehicle and passenger safety. The article reports the development of a classification module for VANET Intrusion Detection Systems (IDSs), which continuously monitors and analyzes data traffic to identify patterns indicative of malicious behavior within the network. In course of this research seven different classifiers were trained using the Basic Safety Message subset of the VerReMi dataset. The performance of these classifiers was comprehensively analyzed based on four metrics. Eventually, the kNN, and Random Forest models ensured the best average performance in the case of both investigated message types.

关键词： Measurement computational modeling Vehicular ad hoc networks Intrusion detection Forestry Traffic control Road safety

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On the impact of population density and mobility restrictions in the control of epidemic spreading

On the impact of population density and mobility restriction...

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International symposium on Applied computational intelligence and Informatics ( SACI)

作者： Ashley Hurrelbrink Alexandru Topirceanu Department of Computer and Information Technology Politehnica University Timişoara Timişoara Romania

Modern computational epidemiology uses compartmental models and agent-based modeling to provide cutting-edge computational tools to track and control epidemic outbreaks. In this sense, many recent global studies have underlined the critical role of population density and mobility restrictions in reducing the spread of an epidemic. In this study, we define a web-based simulation framework based on an original agent-based model and the SICARS epidemic model to investigate the relationship between population density and individual mobility restrictions in terms of reducing the intensity of an infectious outbreak. By analyzing the results of discrete event simulations, we provide a perspective on how these two factors influence the spread of the disease and reduce its impact on the population. We conclude that both density and mobility play a crucial, yet equivalent, role in the dynamics of epidemic spreading, determining a logarithmic increase in the amplitude of the highest infectious wave. The results of the study are expected to provide valuable insights to policy makers and public health officials on how to manage future outbreaks.

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Multi-Objective Structure and Parameter Evolution of Neuro-Fuzzy Systems

Multi-Objective Structure and Parameter Evolution of Neuro-F...

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ieee symposium Series on computational intelligence (ieee SSCI)

作者： Moshaiov, Amiram Salih, Adham Tel Aviv Univ Sch Mech Engn Tel Aviv Israel

ISBN: (纸本)9781728190488

This paper presents an evolutionary algorithm that allows the adaptation of neuro-fuzzy systems according to conflicting objectives. The adaptation includes both the structures and the parameters of the evolved solutions. The suggested algorithm, which is termed Fuzzy-Evolution of Membership and Structures by Decomposition (FEMS/D). As suggested by its name, the algorithm is based on the decomposition approach, which is a divide-and-conquer technique that transforms the original multi-objective problem into a set of single-objective sub-problems. In addition, this paper provides initial demonstrations of the applicability of FEMS/D to the development of non-dominated robot motion controllers, which exhibit a range of behaviors between the safest and the fastest ones. Finally, the adaptation of the controllers to an abrupt change in the environment is also demonstrated.

关键词： Many-objective Optimization Decomposition Approach Pareto-optimization Fuzzy-Inference System Robot Motion-control

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4th World Robot Conference (WRC) / symposium on Advanced Robotics and automation (SARA)

作者： Zhang, Xiaowen Yuan, Shuai Luan, Fangjun Lv, Jiaqi Liu, Guifu Shenyang Jianzhu Univ Sch Elect & Control Engn Shenyang 110168 Peoples R China Shenyang Jianzhu Univ Sch Comp Sci & Engn Shenyang 110168 Peoples R China

ISBN: (数字)9781665463690

ISBN: (纸本)9781665463690

Ship detection has always been an important and challenging task. Small ship targets and complex backgrounds in optical remote sensing images can both lead to false alarms and missed alarms in detection. The YOLO series algorithms have been widely used in optical remote sensing ship target detection, which has the advantage of fast detection speed. However, the YOLO series algorithms have poor detection performance when facing small targets. Therefore, we propose a new algorithm, SMMA-YOLOv5, which can improve the model detection accuracy without significantly increasing the model size. We first introduce a self-attention mechanism to replace some of the convolutional layers to capture the global information of the feature map. Second, we integrate an efficient channel attention (ECA) model to the self-attention mechanism to enable information interaction between channels without adding additional computational overhead. Furthermore, we propose a new similarity mask structure to filter out the invalid regions in the feature map based on the elements' similarity. The experiments on the public MASATI ship dataset indicate that SMMA-YOLOv5 improves Precision by 3.9%, Recall by 5.3%, and AP by 5.4%, and prove the effectiveness of the algorithm while maintaining real-time detection.

关键词： Optical filters computational modeling Object detection Filtering algorithms Optical imaging Information filters Real-time systems

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Compressing Interpretable Representations of Piecewise Linear Neural Networks using Neuro-Fuzzy Models

Compressing Interpretable Representations of Piecewise Linea...

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ieee symposium Series on computational intelligence (ieee SSCI)

作者： Glass, Leon Hilali, Wael Nelles, Oliver Robert Bosch GmbH Corp Res Renningen Germany Univ Siegen Automat Control Mechatron Siegen Germany

ISBN: (纸本)9781728190488

We present Rectified Linear Unit based Local Linear Model Tree (ReLUMoT). A model that bridges the gap between Piecewise Linear Neural Networks (PLNN) and Local Model Networks (LMN) like those resulting from the LoLiMoT algorithm. Essentially, we perform the input space partitioning of LoLiMoT by training a PLNN and extracting its linear regions. These become the input space partitions of ReLUMoT. From the perspective of PLNNs our approach compresses and smoothens low-dimensional models, while making them interpretable. From the perspective of LoLiMoT, our approach replaces the incremental and heuristic input space partitioning scheme with gradient-based training of a neural network, which is considerably more flexible.

关键词： Explainable AI Fuzzy Local Model Trees Neuro-Fuzzy Models Piecewise Linear Neural Networks

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Tabu Search Algorithm for Cost Optimization in Multimodal Transport

Tabu Search Algorithm for Cost Optimization in Multimodal Tr...

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International symposium on computational intelligence and Informatics

作者： Radosław Idzikowski Michał Jaroszczuk Piotr Nowak Mariusz Uchroński Department of Automatics Mechatronics and Control Systems Faculty of Electronics Wroclaw University of Science and Technology Wroclaw Poland Wroclaw Centre for Networking and Supercomputing Wroclaw University of Science and Technology Wroclaw Poland

ISBN: (数字)9798350353433

ISBN: (纸本)9798350353440

In presented article, the multimodal transport problem between a set of transshipment depots and a set of final recipients using non-homogenous vehicle fleet is being considered. A mathematical model was proposed with real-life constraints such as load and capacity limitations, the number of vehicles in a given depot, and the inability to divide the order. As solving methods, greedy and Tabu Search algorithms were implemented. The results show that for given problem Tabu Search can improve initial solution obtained by greedy algorithm in all cases. For Tabu Search algorithm, applying aspiration criteria, bigger iteration number and longer tabu list, can lead to better results.

关键词： Greedy algorithms Costs Search problems Mathematical models Informatics Optimization computational intelligence

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Generating Cardiovascular Data by Integration of Physiological Information

Generating Cardiovascular Data by Integration of Physiologic...

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2025 ieee symposium on computational intelligence in Health and Medicine, CIHM 2025

作者： Kummert, Johannes Schulz, Alexander Habigt, Moriz Stemmler, Maike Kohler, Christina Abel, Dirk Rossaint, Rolf Hammer, Barbara Machine Learning Group Bielefeld University Bielefeld Germany Anaesthesiology Clinic RWTH Aachen University Aachen Germany Institute of Automatic Control RWTH Aachen University Aachen Germany

ISBN: (纸本)9798331508333

Medical data is a valuable resource that is under privacy constraints and often requires a substantial effort and cost to obtain. This is the case for physiological cardiovascular data that is among others utilized to develop assistive heart devices. In the present work, we aim to improve a GAN based generative approach from the literature by integrating physiological knowledge in form of pressure-value curves together with a dynamic time warping cost function into the model. We demonstrate that our adapted model generates more realistic cardiovascular signals as measured by targeted physiological markers and showcase that such generated signals can be used to improve a downstream task on real data. © 2025 ieee.

关键词： Heart

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Efficient solution method based on inverse dynamics for optimal control problems of rigid body systems

Efficient solution method based on inverse dynamics for opti...

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ieee International Conference on Robotics and automation (ICRA)

作者： Katayama, Sotaro Ohtsuka, Toshiyuki Kyoto Univ Grad Sch Informat Dept Syst Sci Kyoto Japan

ISBN: (纸本)9781728190778

We propose an efficient way of solving optimal control problems for rigid-body systems on the basis of inverse dynamics and the multiple-shooting method. We treat all variables, including the state, acceleration, and control input torques, as optimization variables and treat the inverse dynamics as an equality constraint. We eliminate the update of the control input torques from the linear equation of Newton's method by applying condensing for inverse dynamics. The size of the resultant linear equation is the same as that of the multiple-shooting method based on forward dynamics except for the variables related to the passive joints and contacts. Compared with the conventional methods based on forward dynamics, the proposed method reduces the computational cost of the dynamics and their sensitivities by utilizing the recursive Newton-Euler algorithm (RNEA) and its partial derivatives. In addition, it increases the sparsity of the Hessian of the Karush-Kuhn-Tucker conditions, which reduces the computational cost, e.g., of Riccati recursion. Numerical experiments show that the proposed method outperforms state-of-the-art implementations of differential dynamic programming based on forward dynamics in terms of computational time and numerical robustness.

关键词： Sensitivity automation Heuristic algorithms Conferences Optimal control Robustness computational efficiency

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Directed Information Flow in Computing Systems with Living Neurons

Directed Information Flow in Computing Systems with Living N...

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Information Theory Workshops (ISIT-W), ieee International symposium on

作者： Austin R. Ellis-Mohr Lav R. Varshney University of Illinois Urbana-Champaign

ISBN: (数字)9798350348446

ISBN: (纸本)9798350348453

There is growing interest in building artificial computational fabrics from biological neurons, a kind of cultured intelligence. We briefly review the significant experimental advances needed to construct such computing systems with living neurons, which are also called synthetic neurocomputers. These novel systems are designed for energy-efficient implementation of numerous embodied intelligence applications, but also enable highly-controlled experimentation to investigate questions in neuroscience itself. The concept of information flow—fundamental in computation and control—is central to this investigation, now manipulable in biological neural networks with unprecedented flexibility and precision. The paper discusses strategies for modulating information flow through structural connectivity and neural plasticity, and presents a case study on information-theoretic analysis of information flow. It concludes by discussing research directions opened by the ability to precisely control information flow in living neurons, proposing future investigations into whether neural systems operate close to their informational limits within the constraints of their physical makeup and future design principles for constructing synthetic neurocomputers.

关键词： Reviews Conferences Neurons computational neuroscience Buildings Neural engineering Fabrics

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OSVAE-GAN: Orthogonal Self-Attention Variational Autoencoder Generative Adversarial Networks for Time Series Anomaly Detection

OSVAE-GAN: Orthogonal Self-Attention Variational Autoencoder...

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ieee symposium Series on computational intelligence (SSCI)

作者： Zhi Li Danya Xu Yuzhe Li Tianyou Chai Tao Yang State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China

Time series anomaly detection is a binary classification problem with unbalanced data, which aims to identify data that fall outside of the normal behaviors. Since the proportion of the abnormal data is very small, the cost of labeling all data is prohibitively high. Therefore, unsupervised methods are more suitable than supervised methods. With the rapid development of deep learning, various multivariate time series anomaly detection methods based on deep learning have been proposed. However, existing methods do not fully capture the spatial-temporal correlations and are not robust to noise. To address these issues, we propose an unsupervised anomaly detection method called Orthogonal Self-Attention Variational Autoencoder Generative Adversarial Networks (OSVAE-GAN). To fully extract the spatial-temporal correlations, we use an orthogonal self-attention (OS) mechanism. Moreover, to increase the capability to deal with complex multivariate data, we integrate two generative adversarial networks (GANs) with the variational autoencoder (VAE). Finally, to reduce the influence of noise, we introduce the maximum mean discrepancy (MMD) loss. Experiments are conducted on five public datasets, which show that the proposed method is superior to the existing methods.

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