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Proceedings of the 7th International Conference on Sustainable Information Engineering and Technology

作者： Katalin M. Hangos Systems and Control Laboratory Institute for Computer Science and Control Hungary and Department of Electrical Engineering and Information Systems University of Pannonia Hungary

ISBN: (纸本)9781450397117

Decomposition offers the potential to reduce the complexity of model-based optimization, prediction, control and diagnosis by accounting for the structure and sparsity of the describing model. Motivated by this fact, a rich and powerful collection of decomposition methods are available for model based diagnosis of large-scale complex dynamic systems, too. At the same time, one usually does not have enough information about a large-scale complex dynamic system to construct its precise enough model, so a kind of qualitative dynamic model is often used for the diagnosis [1]. Two structural decomposition based qualitative diagnostic methods are presented in this lecture, together with their component-driven system decomposition ***, a model-based diagnostic method is described that is able to detect and isolate non-technical losses (illegal loads) in low voltage electrical grids of one transformer area [2]. As a preliminary off-line step of the diagnosis, a powerful electrical decomposition method is proposed, which breaks down the overall network to subsystems with one feeder layout enabling to make the necessary computations efficient. The diagnostic method is based on analyzing the differences between the measured and model-predicted voltages. The uncertainty in the model parameters together with the measurement uncertainties are also taken into account to make the approach applicable in real-world cases. The proposed method is able to detect and localize multiple illegal loads, and the amount of the illegal consumption can also be *** a second case study, a high level decomposition approach for process system fault diagnosis using event traces is given [3], [4]. Using a simple component graph model behind the process system and the measured trace applied for the diagnosis, the method can find the root cause(s) of propagating failures between separate components. The method can connect individually operating lower-level component-specific diag

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Domain-specific data characteristics: A study on meaning of stylometric sub-concepts and in-class imbalance

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Procedia computer Science 2024年 246卷 4421-4430页

作者： Urszula Stańczyk Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A 44-100 Gliwice Poland

In the context of data imbalance probably the most investigated problem is imbalance of classes, as learning from the data with this characteristic makes detection of existing patterns for all classes more difficult. However, other problems related to imbalance also exists and the paper addresses such cases where classes are balanced, but there is in-class imbalance. Such imbalance can be caused by uneven representation of sub-concepts. When there is a noticeable difference between the numbers of samples belonging to sub-concepts, this can turn the under-represented sub-concepts into disjuncts. Data irregularities of this type can hinder recognition, therefore actions are typically taken to restore balance. In the investigations described, the issue was studied in the stylometric domain and various classifiers were applied to the data that was balanced, then imbalanced, and finally with restored balance. The experiments show that the specifics of the domain of application can put its own mark on the data which is difficult to overcome by standard processing such as under- or oversampling. Observed dependence on a learner and dataset makes the issue even more complex and layered, and shows the need for deeper studies.

关键词： Attribute domain Data irregularities Sub-concept Disjunct Pattern recognition Stylometry Authorship attribution

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Solar Wind Density Forecasting with U-Net and LSTM-based Neural Networks 8

Solar Wind Density Forecasting with U-Net and LSTM-based Neu...

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8th International Conference of Yearly Reports on Informatics, Mathematics, and Engineering, ICYRIME 2023

作者： Iacobelli, Emanuele Fiani, Francesca Napoli, Christian Department of Computer Control and Management Engineering Sapienza University of Rome Roma00185 Italy Institute for Systems Analysis and Computer Science Italian National Research Council Roma00185 Italy Department of Computational Intelligence Czestochowa University of Technology Czestochowa42-201 Poland

Forecasting changes in solar wind properties accurately is crucial for predicting space weather, as it significantly impacts the majority of space operations and the telecommunication system. To meet this challenge, we introduce an architecture that combines U-Net’s capabilities for segmenting coronal holes from high-resolution sun images with the predictive abilities of Long Short-Term Memory (LSTM) and ConvLSTM models. This architecture predicts solar wind density using sun surface images obtained from the AIA 193 Å dataset (provided by NASA) and historical electron and proton density data from the OMNI and ELM2 datasets (also provided by NASA), covering the entire year 2012. Our findings demonstrate the system’s ability to generate reliable coronal hole segmentation maps and achieve good accuracy in forecasting solar wind density. © 2023 Copyright for this paper by its authors.

关键词： NASA

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Enhancing Object Detection Robustness for Cross-Depiction Through Neural Style Transfer 8

Enhancing Object Detection Robustness for Cross-Depiction Th...

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8th International Conference of Yearly Reports on Informatics, Mathematics, and Engineering, ICYRIME 2023

作者： Fiani, Francesca Puglisi, Adriano Napoli, Christian Department of Computer Control and Management Engineering Sapienza University of Rome Roma00185 Italy Institute for Systems Analysis and Computer Science Italian National Research Council Roma00185 Italy Department of Computational Intelligence Czestochowa University of Technology Czestochowa42-201 Poland

Modern neural networks models for computer vision are trained on millions of images. The idea is that models are able to increase generalization when the dataset contains well diversified images, e.g. with varied illumination and environmental conditions of the same objects. Generalization is particularly relevant in object detection, especially for what concerns the cross-depiction problem. In this work we explore the use of Neural Style Transfer as a novel technique to morph the original data, with the aim to enhance model generalization. To verify the effect on performances for object detection models, we selected the Faster R-CNN model to be applied on the Pascal VOC 2012 dataset. A number of tests were performed through style variations on images and by tuning Neural Style Transfer parameters to maintain the content of the original images. The experiments showed promising results, which effectively provide a foundation for future studies on cross-depiction via Neural Style Transfer. © 2023 Copyright for this paper by its authors.

关键词： Object detection

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Pole Identification using discrete Laguerre expansion and variable projection

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IFAC-PapersOnLine 2023年第2期56卷 3994-3999页

作者： Tamás Dózsa Mátyás Szabari Alexandros Soumelidis Péter Kovács Institute for Computer Science and Control Systems and Control Laboratory Eötvös Loránd University Department of Numerical Analysis Eötvös Loránd University Faculty of Science Institute for Computer Science and Control Systems and Control Laboratory Eötvös Loránd University Department of Numerical Analysis

We propose a novel algorithm for identifying the poles of transfer functions describing SISO-LTI (single input single output, linear time invariant) systems. Our Identification method works in the frequency domain and consists of two parts. In the first part, we extend a discrete Laguerre expansion based method with an automatic parameter selection scheme. This allows us to find an initial estimate of the poles of SISO-LTI transfer functions without the need for human intuition. Then, in the second part, we propose a novel optimization problem to improve our initial estimates. The proposed optimization aims to reduce the least squared error of a parameterized model, which can be interpreted as an orthogonal projection of the system's frequency response onto a subspace spanned by Generalized Orthogonal Rational Basis functions (GOBFs). We solve the corresponding nonlinear optimization task using gradient based methods, where we can analytically calculate the gradient of the error functional. Through robust numerical experiments, we investigate the behavior of the developed methods and show that they work even in scenarios, when the transfer function has a high number of poles.

关键词： Frequency domain identification Recursive identification Linear systems Variable Projection Generalized Orthogonal Basis Functions, Malmquist-Takenaka functions

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Observations of data characteristics and irregularities through domain-oriented transformations of attributes

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Procedia computer Science 2024年 246卷 2694-2703页

作者： Urszula Stańczyk Grzegorz Baron Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A 44-100 Gliwice Poland

The paper presents research dedicated to observations of relations between attribute properties and discretisation. In the investigations described, the gradually increasing sets of features were discretised by selected approaches, and several variants of data were constructed. The continuous, partially discrete, and completely translated datasets were explored by the chosen classifiers and their performance studied in the context of a number of discretised attributes, discretisation procedures, and the way of processing of features and datasets. The stylometric problem of authorship attribution was the machine learning task under study. The experimental results enable to observe closer the specificity of style-markers employed as characteristic features, and indicate conditions for efficient recognition of authorship. They can be extended to other application domains with similar characteristics.

关键词： Attribute domain Discretisation Data representation Data irregularities Pattern recognition Stylometry Authorship attribution

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Laundry Cluster Management Using Cloud

Laundry Cluster Management Using Cloud

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Federated Conference on computer Science and Information systems (FedCSIS)

作者： Mateusz Salach Bartosz Trybus Bartosz Pawłowicz Marcin Hubacz Department of Complex Systems Rzeszow University of Technology Rzeszow Department of Computer and Control Engineering Department of Electronic and Telecommunications Systems

Electronic devices in the 21st century have numerous network components, including wireless or wired Internet access modules. Connecting devices to networks and cloud services enables them to access new functionalities and unlock system updates and device security enhancements. The article presents the concept of an intelligent laundry management system based on RFID and cloud computing. The Internet connection not only unlocks additional features of the washing machine, such as different washing modes, but also allows for selecting the appropriate detergent level and washing parameters based on the textile material being washed. Additionally, the paper presents the solution and measurement studies on the accuracy of textile identification.

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Learning neural contracting dynamics: extended linearization and global guarantees 24

Learning neural contracting dynamics: extended linearization...

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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Sean Jaffe Alexander Davydov Deniz Lapsekili Ambuj K. Singh Francesco Bullo Center for Control Dynamical Systems and Computation University of California Santa Barbara and Department of Computer Science University of California Santa Barbara Center for Control Dynamical Systems and Computation University of California Santa Barbara Department of Computer Science University of California Santa Barbara

ISBN: (纸本)9798331314385

Global stability and robustness guarantees in learned dynamical systems are essential to ensure well-behavedness of the systems in the face of uncertainty. We present Extended Linearized Contracting Dynamics (ELCD), the first neural network-based dynamical system with global contractivity guarantees in arbitrary metrics. The key feature of ELCD is a parametrization of the extended linearization of the nonlinear vector field. In its most basic form, ELCD is guaranteed to be (i) globally exponentially stable, (ii) equilibrium contracting, and (iii) globally contracting with respect to some metric. To allow for contraction with respect to more general metrics in the data space, we train diffeomorphisms between the data space and a latent space and enforce contractivity in the latent space, which ensures global contractivity in the data space. We demonstrate the performance of ELCD on the high dimensional LASA, multi-link pendulum, and Rosenbrock datasets.

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Metaheuristic Optimization for Mobile Robot Navigation Based on Path Planning

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computers, Materials & Continua 2022年第11期73卷 2241-2255页

作者： El-Sayed M.El-kenawy Zeeshan Shafi Khan Abdelhameed Ibrahim Bandar Abdullah Aloyaydi Hesham Arafat Ali Ali E.Takieldeen Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Science Department College of Engineering and Computer ScienceMustaqbal UniversityBuraidahSaudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Mechanical Engineering Qassim UniversityBuraidah51452Saudi Arabia

Recently,the path planning problem may be considered one of the most interesting researched topics in autonomous *** is why finding a safe path in a cluttered environment for a mobile robot is a significant requisite.A promising route planning for mobile robots on one side saves time and,on the other side,reduces the wear and tear on the robot,saving the capital *** route planning methods for the mobile robot have been developed and *** to our best knowledge,no method offers an optimum solution among the existing *** Swarm Optimization(PSO),a numerical optimization method based on the mobility of virtual particles in a multidimensional space,is considered one of the best algorithms for route planning under constantly changing environmental *** the researchers,reactive methods are increasingly common and extensively used for the training of neural networks in order to have efficient route planning for mobile *** paper proposes a PSO Weighted Grey Wolf Optimization(PSOWGWO)*** is a hybrid algorithm based on enhanced Grey Wolf Optimization(GWO)with *** order to measure the statistical efficiency of the proposed algorithm,Wilcoxon rank-sum and ANOVA statistical tests are *** experimental results demonstrate a 25%to 45%enhancement in terms of Area Under Curve(AUC).Moreover,superior performance in terms of data size,path planning time,and accuracy is demonstrated over other state-of-the-art techniques.

关键词： Mobile robot swarm optimization robot route planning neural networks

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A Multi-Player Potential Game Approach for Sensor Network Localization with Noisy Measurements 63

A Multi-Player Potential Game Approach for Sensor Network Lo...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Xu, Gehui Chen, Guanpu Fidan, Baris Hong, Yiguang Qi, Hongsheng Parisini, Thomas Johansson, Karl H. Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm100 44 Sweden University of Waterloo Department of Mechanical and Mechatronics Engineering WaterlooONN2L 3G1 Canada Tongji University Department of Control Science and Engineering shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China University of Chinese Academy of Sciences School of Mathematical Sciences Beijing China Aalborg University Department of Electronic Systems Denmark Imperial College London Department of Electrical and Electronic Engineering LondonSW7 2AZ United Kingdom University of Trieste Department of Engineering and Architecture Italy

ISBN: (纸本)9798350316339

Sensor network localization (SNL) is a challenging problem due to its inherent non-convexity and the effects of noise in inter-node ranging measurements and anchor node position. We formulate a non-convex SNL problem as a multiplayer non-convex potential game and investigate the existence and uniqueness of a Nash equilibrium (NE) in both the ideal setting without measurement noise and the practical setting with measurement noise. We first show that the NE exists and is unique in the noiseless case, and corresponds to the precise network localization. Then, we study the SNL for the case with errors affecting the anchor node position and the inter-node distance measurements. Specifically, we establish that in case these errors are sufficiently small, the NE exists and is unique. It is shown that the NE is an approximate solution to the SNL problem, and that the position errors can be quantified accordingly. Based on these findings, we apply the results to case studies involving only inter-node distance measurement errors and only anchor position information inaccuracies. © 2024 IEEE.

关键词： Nash equilibrium

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