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International Conference on Wavelet Active Media technology and Information Processing (ICWAMTIP)

作者： Xu Jiayi Yu Jiaqi Wang Yixin Han Shijia School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Beijing Institute of Control and Electronic Technology Beijing China

ISBN: (数字)9798331519254

ISBN: (纸本)9798331519261

Cross-domain object detection poses significant challenges due to the susceptibility of object detection models to data variance, particularly the domain shifts that can occur between different domains. To address the limitation, we draw inspiration from knowledge distillation, proposing a collaborative learning framework. Our method employs CycleGAN to generate target-style images, and during pretraining, an unsupervised domain adaptation teacher model is trained for each source-target pair. In the distillation process, our proposed algorithm implements an out-of-distribution estimation strategy to select samples that best align with the current model, thereby enhancing the cross-domain distillation process. Furthermore, each expert model is encouraged to collaborate by designating the student model as a bridge between different target domains, facilitated by the Exponential Moving Average (EMA) algorithm. Experiments show that the proposed method leverages structured information, not only does it perform well across various target domains, but it also yields favorable results compared to state-of-the-art unsupervised methods that are specifically trained on single source-target pair.

关键词： Bridges Adaptation models Federated learning Large language models Estimation Object detection Information processing Media Explosions Periodic structures

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On Space-Filling Input Design for Nonlinear Dynamic Model Learning: A Gaussian Process Approach

arXiv

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arXiv 2025年

作者： Liu, Yuhan Kiss, Máté Tóth, Roland Schoukens, Maarten Control Systems Group Department of Electrical Engineering Eindhoven University of Technology Eindhoven5600 MB Netherlands Systems and Control Laboratory HUN-REN Institute for Computer Science and Control Budapest1111 Hungary

While optimal input design for linear systems has been well-established, no systematic approach exists for nonlinear systems, where robustness to extrapolation/interpolation errors is prioritized over minimizing estimated parameter variance. To address this issue, we develop a novel identification-oriented space-filling input design strategy for efficient covering of a well-defined region of interest. By placing a Gaussian Process (GP) prior on the joint input-state space, the proposed strategy leverages the GP posterior variance to construct a space-filling promoting input design cost function from the Bayesian point of view. This accordingly enables maximization of the coverage in the region of interest, thereby facilitating the generation of informative datasets. Furthermore, we theoretically prove that minimization of the cost function indicates the space-filling behavior. The effectiveness of the proposed strategy is demonstrated on both an academic example and a mass-spring-damper system, highlighting its potential practical impact on efficient exploration in identification scenarios for nonlinear dynamical systems. © 2025, CC BY-NC-SA.

关键词： Cost functions

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RESIDUAL LIFE PREDICTION OF LITHIUM BATTERY BASED ON IMPROVED MULTI-OPTIMIZATION ALGORITHM AND SUPPORT VECTOR REGRESSION 14

RESIDUAL LIFE PREDICTION OF LITHIUM BATTERY BASED ON IMPROVE...

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14th International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, QR2MSE 2024

作者： Tao, Yulong Wang, Jun Wang, Kai School of Computer Science and Technology Shenyang University of Chemical Technology Shenyang China Key Laboratory of Intelligent Technology for Chemical Process Industry Liaoning Province Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Robotics and Artificial Intelligence Manufacturing Innovation Institute Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9781837242672

The lithium-ion battery is increasingly critical in the fields of electric vehicles and sustainable energy. Accurate prediction of the Remaining Useful Life (RUL) of lithium-ion batteries is essential to mitigate risks and minimize potential losses. This paper introduces a novel fusion algorithm, FPSOGWO, which combines Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) with an enhanced Circle chaos strategy and a random adaptive weighting strategy. The FPSOGWO algorithm, integrated with Support Vector Regression (FPSOGWO-SVR), was utilized to predict the RUL of health factors in lithium batteries. Compared to other optimization algorithms, FPSOGWO exhibits superior optimization speed and value, effectively overcoming issues of local optimality. Specifically, when benchmarked against the Whale Optimization Algorithm-SVR (WOA-SVR), the R-Square values of FPSOGWO-SVR showed improvements of 15.62%, 1.28%, 5.67%, and 10.65% across four different types of batteries. Experimental results demonstrate that FPSOGWO-SVR provides more accurate RUL predictions for lithium batteries, highlighting its potential for broader application in battery health management systems. © The Institution of Engineering & technology 2024.

关键词： Support vector regression

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Equilibrium-Independent control of Continuous-Time Nonlinear Systems Via the LPV Framework

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IEEE Transactions on Automatic control 2025年

作者： Koelewijn, Patrick J. W. Weiland, Siep Toth, Roland Eindhoven University of Technology Control Systems Group Department of Electrical Engineering Eindhoven Netherlands Institute for Computer Science and Control Systems and Control Laboratory Budapest Hungary

In this paper, we consider the analysis and control of continuous-time nonlinear systems to ensure universal shifted stability and performance, i.e., stability and performance w.r.t. each forced equilibrium point of the system. This "equilibrium-free"concept is especially beneficial for control problems that require the tracking of setpoints and rejection of persistent disturbances, such as input loads. In this paper, we show how the velocity form, i.e., the time-differentiated dynamics of the system, plays a crucial role in characterizing these properties and how the analysis of it can be solved by the application of Linear Parameter-Varying (LPV) methods in a computationally efficient manner. Furthermore, by leveraging the properties of the velocity form and the LPV framework, a novel controller synthesis method is presented which ensures closed-loop universal shifted stability and performance. The proposed controller design is verified experimentally on a real system. Additionally, we compare the proposed method to a standard LPV control design, demonstrating the improved stability and performance guarantees of the new approach. © 1963-2012 IEEE.

关键词： control system synthesis

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Two-Step Online Trajectory Planning of a Quadcopter in Indoor Environments with Obstacles

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IFAC-PapersOnLine 2023年第2期56卷 11002-11009页

作者： M. Zimmermann M.N. Vu F. Beck A. Nguyen A. Kugi Automation and Control Institute (ACIN) TU Wien Vienna Austria Center for Vision Automation and Control AIT Austrian Institute of Technology Vienna Austria Department of Computer Science University of Liverpool Liverpool England

This paper presents a two-step algorithm for online trajectory planning in indoor environments with unknown obstacles. In the first step, sampling-based path planning techniques such as the optimal Rapidly exploring Random Tree (RRT*) algorithm and the Line-of-Sight (LOS) algorithm are employed to generate a collision-free path consisting of multiple waypoints. Then, in the second step, constrained quadratic programming is utilized to compute a smooth trajectory that passes through all computed waypoints. The main contribution of this work is the development of a flexible trajectory planning framework that can detect changes in the environment, such as new obstacles, and compute alternative trajectories in real time. The proposed algorithm actively considers all changes in the environment and performs the replanning process only on waypoints that are occupied by new obstacles. This helps to reduce the computation time and realize the proposed approach in real time. The feasibility of the proposed algorithm is evaluated using the Intel Aero Ready-to-Fly (RTF) quadcopter in simulation and in a real-world experiment.

关键词： quadcopter motion planning optimal rapidly exploring random tree (RRT*) constrained quadratic programming differential flatness trajectory planning

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Domain Adapted Object Detection with Intermediate Domain Generation

Domain Adapted Object Detection with Intermediate Domain Gen...

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International Conference on Wavelet Active Media technology and Information Processing (ICWAMTIP)

作者： He Yuhang Sun Yangyang Xue Zhijun Han Shijiao School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Beijing Institute of Control and Electronic Technology Beijing China

ISBN: (数字)9798331519254

ISBN: (纸本)9798331519261

Autonomous driving detection technology in real-world road scenarios faces numerous challenges, including variable weather conditions and complex road environments. Therefore, developing an object detection model with robust domain adaptation is crucial. In this paper, we investigate the use of image style transfer techniques to leverage target domain images for enhancing model performance. Experimental results show that our proposed approach significantly improves detection efficacy. Notably, our method outperforms the Oracle results in tasks such as transitioning from Cityscapes to Foggy Cityscapes, highlighting its effectiveness in addressing domain adaptation challenges.

关键词： Training Adaptation models Roads Object detection Information processing Detectors Media Autonomous vehicles Faces Meteorology

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Port-Hamiltonian Neural Networks with Output Error Noise Models

arXiv

引用

arXiv 2025年

作者： Moradi, Sarvin Beintema, Gerben I. Jaensson, Nick Tóth, Roland Schoukens, Maarten Control System group Eindhoven University of Technology Eindhoven Netherlands Eindhoven Artificial Intelligence Systems Institute Eindhoven Netherlands Processing and Performance of Materials Group Eindhoven University of Technology Eindhoven Netherlands Systems and Control Laboratory HUN-REN Institute for Computer Science and Control Budapest Hungary

Hamiltonian neural networks (HNNs) represent a promising class of physics-informed deep learning methods that utilize Hamiltonian theory as foundational knowledge within neural networks. However, their direct application to engineering systems is often challenged by practical issues, including the presence of external inputs, dissipation, and noisy measurements. This paper introduces a novel framework that enhances the capabilities of HNNs to address these real-life factors. We integrate port-Hamiltonian theory into the neural network structure, allowing for the inclusion of external inputs and dissipation, while mitigating the impact of measurement noise through an output-error (OE) model structure. The resulting output error port-Hamiltonian neural networks (OE-pHNNs) can be adapted to tackle modeling complex engineering systems with noisy measurements. Furthermore, we propose the identification of OE-pHNNs based on the subspace encoder approach (SUBNET), which efficiently approximates the complete simulation loss using subsections of the data and uses an encoder function to predict initial states. By integrating SUBNET with OE-pHNNs, we achieve consistent models of complex engineering systems under noisy measurements. In addition, we perform a consistency analysis to ensure the reliability of the proposed data-driven model learning method. We demonstrate the effectiveness of our approach on system identification benchmarks, showing its potential as a powerful tool for modeling dynamic systems in real-world applications. © 2025, CC BY-SA.

关键词： Hamiltonians

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From News to Summaries: Building a Hungarian Corpus for Extractive and Abstractive Summarization

arXiv

引用

arXiv 2024年

作者： Barta, Botond Lakatos, Dorina Nagy, Attila Nyist, Milán Konor Ács, Judit HUN-REN Institute for Computer Science and Control Department of Automation and Applied Informatics Budapest University of Technology and Economics Hungary

Training summarization models requires substantial amounts of training data. However for less resourceful languages like Hungarian, openly available models and datasets are notably scarce. To address this gap our paper introduces HunSum-2 an open-source Hungarian corpus suitable for training abstractive and extractive summarization models. The dataset is assembled from segments of the Common Crawl corpus undergoing thorough cleaning, preprocessing and deduplication. In addition to abstractive summarization we generate sentence-level labels for extractive summarization using sentence similarity. We train baseline models for both extractive and abstractive summarization using the collected dataset. To demonstrate the effectiveness of the trained models, we perform both quantitative and qualitative evaluation. Our dataset, models and code are publicly available, encouraging replication, further research, and real-world applications across various domains. © 2024, CC BY.

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A Novel Sky-Aware Dehazing Approach for Outdoor Autonomous Systems

A Novel Sky-Aware Dehazing Approach for Outdoor Autonomous S...

引用

Software Engineering and computer Science (CSECS), International Conference on

作者： Xiaoyan Wu Dali Xu Fangfa Fu Bing Yang Hongyang Liu Kanglong Liu Xiyu Ding Chao Wu Na Niu School of Computer and Control Engineering Northeast Forestry University Harbin China School of Astronautics Harbin Institute of Technology Harbin China School of Computer Science Harbin University of Science and Technology Harbin China

ISBN: (数字)9798331522216

ISBN: (纸本)9798331522223

Clear outdoor images are essential for autonomous driving and accurate target detection, especially in haze. The majority of algorithms are unable to adequately address the issue of dehazing, resulting in a range of distortions, particularly in the sky area. This paper proposes an advanced dehazing algorithm for enhancing sky-area visuals (ESV). We segment the image into sky and non-sky areas, with atmospheric light levels being determined within the sky area. To enhance the recovery of the sky region, we suggest fine-tuning the sky's transmission to a predetermined constant value. Ultimately, the dehazed image is retrieved utilizing the atmospheric scattering model. Extensive experiments have shown that our proposed algorithm outperforms alternative methods, increasing PSNR by up to 1.3733%, 1.6360%, 2.4169%, 0.9512%, SSIM by up to 4.8995%, 0.6281%, 6.5335%, 8.7165%, enhancing the visuals of sky-area, compared to DCP, CAP, HC-CEP and AOD-Net.

关键词： Visualization Atmospheric modeling Software algorithms Scattering Reinforcement learning Object detection Distortion Rendering (computer graphics) Robustness Software engineering

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Intelligent System for Predicting the Integrity of Operating Pipelines

Intelligent System for Predicting the Integrity of Operating...

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International Conference on control Systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Kristina Evseenkova Galina Kuznetsova Department of Computer-Aided Design and Control Systems Saint-Petersburg State Institute of Technology Saint Petersburg Russian Federation

ISBN: (数字)9798331532178

ISBN: (纸本)9798331532185

An intelligent system for monitoring the integrity of operating pipelines is presented. The software package is designed to assess the corrosion rate and calculate the residual life of pipelines, which takes into account the parameters of climatic and transported environments, the characteristics of pipelines and their insulating coatings. The complex allows us to consider different types of corrosion: atmospheric, electrochemical and mechanical. A functional structure is proposed that describes the operating principle of the software package, calculation results and examples of intelligent system interfaces are presented.

关键词： Software packages Atmospheric modeling Corrosion Pipelines Predictive models control systems Mathematical models Energy efficiency Intelligent systems Monitoring

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