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Adaptive Genghis Khan Shark based convolutional denoising autoencoder for quality enhancement of satellite images

International Journal of Information Technology (Singapore) 2025年 1-14页

作者： Rajalakshmi, M. Subramanian, R. S. Sankara Tamilselvan, S. Bhuvanesh, A. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Tamil Nadu Chennai India Department of Mathematics PSG Institute of Technology and Applied Research Tamil Nadu Coimbatore India Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Tamil Nadu Coimbatore India Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli India

Satellite image processing often enhances feature perception and visibility for exact feature identification. Additionally, the change improves remote sensing data quality and clarity. Due to their long distances, these photos often have weather-related noise and distortion that must be fixed. This paper addresses these challenges with a Deep Learning (DL) satellite image denoising technique. An Adaptive Genghis Khan Shark Algorithm (AGKSA) and a Convolutional Denoised Autoencoder are used to improve satellite images in this study. The CDAE settings are optimized using the AGKSA while keeping important image attributes. AGKSA is used in this hybrid model to optimize CDAE performance under various noise conditions. Experimental results showed that the proposed model outperforms traditional image denoising approaches in satellite imagery feature extraction and visual quality. In very precise image processing applications like environmental monitoring and land use classification, the CDAE-AGKSA works well. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.

关键词： Adaptive Genghis Khan algorithm Convolutional denoised autoencoder Satellite image enhancement Satellite image processing

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DR-PETS: Learning-Based Control With Planning in Adversarial Environments

arXiv

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arXiv 2025年

作者： Jesawada, Hozefa Acernese, Antonio Russo, Giovanni Del Vecchio, Carmen Department of Information and Electrical Engineering and Applied Mathematics University of Salerno Italy Department of Engineering University of Sannio Italy

Ensuring robustness against epistemic, possibly adversarial, perturbations is essential for reliable real-world decision-making. While the Probabilistic Ensembles with Trajectory Sampling (PETS) algorithm inherently handles uncertainty via ensemble-based probabilistic models, it lacks guarantees against structured adversarial or worst-case uncertainty distributions. To address this, we propose DR-PETS, a distributionally robust extension of PETS that certifies robustness against adversarial perturbations. We formalize uncertainty via a p-Wasserstein ambiguity set, enabling worst-case-aware planning through a min-max optimization framework. While PETS passively accounts for stochasticity, DR-PETS actively optimizes robustness via a tractable convex approximation integrated into PETS’ planning loop. Experiments on pendulum stabilization and cart-pole balancing show that DR-PETS certifies robustness against adversarial parameter perturbations, achieving consistent performance in worst-case scenarios where PETS deteriorates. © 2025, CC BY-NC-ND.

关键词： Adversarial machine learning

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Detecting Confounders in Multivariate Time Series using Strength of Causation 31

Detecting Confounders in Multivariate Time Series using Stre...

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31st European Signal Processing Conference, EUSIPCO 2023

作者： Liu, Yuhao Cui, Chen Waxman, Daniel Butler, Kurt Djurić, Petar M. Department of Applied Mathematics and Statistics Stony Brook University NY11794 United States Department of Electrical and Computer Engineering Stony Brook University NY11794 United States

ISBN: (纸本)9789464593600

One of the most important problems in science is understanding causation. This is particularly challenging when one has access to observational data only and is further compounded in the presence of latent confounders. In this paper, we propose a method for detecting confounders in multivariate time series using a recently introduced concept referred to as differential causal effect (DCE). The solution is based on feature-based Gaussian processes that are used for estimating both, the DCE of the observed time series and the latent confounders. We demonstrate the performance of the proposed method with several examples. They show that the proposed approach can detect confounders and can accurately estimate causal strengths. © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Time series

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The Neural Network Controller for the Dry Low Emission Combustor of Gas-Turbine Power Plants

The Neural Network Controller for the Dry Low Emission Combu...

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2023 International Russian Smart Industry Conference, SmartIndustryCon 2023

作者： Kuznetsova, T.A. Sukharev, A.A. Perm National Research Polytechnic University Department of Design and Technology in Electrical Engineering Perm Russia Perm National Research Polytechnic University Department of Applied Mathematics Perm Russia

ISBN: (纸本)9781665464291

The study is devoted to the development and testing of the harmful substances' emission controller for a gas turbine power plant with a capacity of 16 MW (GTP-16) based on the built-in neural network mathematical model of a dry low emission (DLE) combustor. The developed algorithms for the neural network controller of the emission of nitrogen oxides, carbon monoxide and pressure pulsations in the DLE-combustor flame tubes are implemented in MATLAB R2018b Simulink and integrated into the GTP-16 automatic control system (ACS) on the hardware/software platform PXI NI. The efficiency of the emissions controller was checked during bench tests on the GTP-16 simulator, with the DLE-combustor neural network model performing the functions of a virtual emissions sensor. The errors in the estimation of emissions and pressure pulsations that meet the accepted requirements are determined. The normal error distribution of the developed neural network model of the combustion chamber is proved. The resulting emission control quality corresponds to the desired one. The conclusion about the possibility and prospects of using neural networks for the development of an adaptive emission control system for DLE-combustors of the gas turbine power plants was made. © 2023 IEEE.

关键词： Carbon monoxide

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Hamiltonian learning using machine-learning models trained with continuous measurements

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Physical Review applied 2024年第4期22卷 044080页

作者： Kris Tucker Amit Kiran Rege Conor Smith Claire Monteleoni Tameem Albash Department of Applied Mathematics Department of Computer Science Center for Quantum Information and Control Department of Electrical and Computer Engineering Paris France Center for Computing Research

We build upon recent work on the use of machine-learning models to estimate Hamiltonian parameters using continuous weak measurement of qubits as input. We consider two settings for the training of our model: (1) supervised learning, where the weak-measurement training record can be labeled with known Hamiltonian parameters, and (2) unsupervised learning, where no labels are available. The first has the advantage of not requiring an explicit representation of the quantum state, thus potentially scaling very favorably to a larger number of qubits. The second requires the implementation of a physical model to map the Hamiltonian parameters to a measurement record, which we implement using an integrator of the physical model with a recurrent neural network to provide a model-free correction at every time step to account for small effects not captured by the physical model. We test our construction on a system of two qubits and demonstrate accurate prediction of multiple physical parameters in both the supervised context and the unsupervised context. We demonstrate that the model benefits from larger training sets, establishing that it is “learning,” and we show robustness regarding errors in the assumed physical model by achieving accurate parameter estimation in the presence of unanticipated single-particle relaxation.

关键词： Entanglement detection Nonlocality Quantum parameter estimation Machine learning Weak measurements

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Robot to Human Interaction with multi-modal conversational engagement

Robot to Human Interaction with multi-modal conversational e...

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IEEE International Conference on Simulation, Modeling, and programming for Autonomous Robots (SIMPAR)

作者： Giuseppe De Simone Luca Greco Alessia Saggese Mario Vento Department of Information and Electrical Engineering and Applied Mathematics (DIEM) University of Salerno Salerno Italy

ISBN: (数字)9798331516857

ISBN: (纸本)9798331516864

In order to increase the social acceptability of social robots, it is important to foster more natural and effective interactions. To achieve this aim, the robot needs to be equipped with Multi-Engagement capabilities, so as to understand if and when people in the scene want to interact with the robot and to detect specifically who wants to interact. In order to face this challenge, in this paper we propose a multimodal architecture, exploiting both audio and video modalities, combining a deep learning based head pose estimator with an active speaker detector. The engagement is thus modeled through a Behavior Tree, which takes into account the history of the engagements with users and decides accordingly for an appropriate behavior of the robot. The proposed architecture has been integrated into a social robot within a ROS framework, running directly on board of an embedded NVIDIA Jetson device. Furthermore, the experimental results confirm that it is able to achieve an F-Score of 0.82 over the widely adopted UE-HRI dataset. Additionally, the system’s human-robot interaction capabilities were assessed by collecting surveys in real-world settings, which indicated a positive impact on user engagement and interaction quality.

关键词： Surveys Deep learning Social robots Detectors programming User experience Dynamic programming History Robots Faces

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Hybrid Iron Loss Model for IPMSMs in Wide-Speed Range Applications 22

Hybrid Iron Loss Model for IPMSMs in Wide-Speed Range Applic...

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22nd International Symposium on Power Electronics, Ee 2023

作者： Banovic, Milica Iricanin, Bratislav Reljic, Dejan Jerkan, Dejan School of Electrical Engineering University of Belgrade The Department of Power Converters and Drives Belgrade Serbia School of Electrical Engineering University of Belgrade The Department of Applied Mathematics Belgrade Serbia University of Novi Sad Faculty of Technical Sciences The Department of Power Electronic and Telecommunication Engineering Novi Sad Serbia

ISBN: (纸本)9798350343175

In this paper a novel hybrid mathematical model for the local iron loss power density calculation method of Interior Permanent Magnets Synchronous Machines (IPMSM) using Finite Element Analysis (FEA) is presented. The proposed model calculates hysteresis losses based on experimentally obtained static hysteresis loops of the ferromagnetic material. This mathematical model provides an alternative for the scarcity of experimental data by estimating hysteresis loops using the Preisach distribution model. The model incorporates waveforms of non-sinusoidal magnetic flux density for each and every finite element individually to precisely calculate eddy current losses. Therefore, its application is suitable for the estimation of local iron loss distribution and its detrimental thermal effects in the wide-speed range operation. The proposed model was verified on an IPMSM machine used for a case study, and it was compared with the conventional method based on Bertotti iron loss model. © 2023 IEEE.

关键词： Finite element method

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DOA-Estimations-Based 3D Geolocations Using Factor Graphs 11

DOA-Estimations-Based 3D Geolocations Using Factor Graphs

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2023 IEEE 11th International Conference on Information, Communication and Networks, ICICN 2023

作者： Qian, Shen Cheng, Meng Kanagawa University Faculty of Informatics Department of Applied Systems and Mathematics Yokohama Japan Shanghai Normal University The College of Information Mechanical and Electrical Engineering Shanghai China

ISBN: (纸本)9798350314014

This paper proposes direction of arrival (DOA)-estimation-based three-dimensional (3D) positioning techniques using factor graphs. The multiple signal classification (MUSIC), root-MUSIC, and estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithms are used to estimate the DOA of the signals received by multiple sensors. A factor graph (FG) detector updates and refines the measured DOA for obtaining the target positions. Since only the mean and the variance of the DOA are needed for the factor graph process, the burden of the data transmission of the uplink from the sensors to the fusion center and the complexity of the positioning is reduced significantly. The effectiveness of the proposed method is demonstrated through simulations. No matter which DOA estimation method is used, MUSIC, root-MUSIC, or ESPRIT, FG algorithms achieve higher positional accuracy compared to the linear least square (LS) positioning solution in terms of the root mean square error (RMSE). © 2023 IEEE.

关键词： Multiple signal classification

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An Efficient Three-Factor Authenticated Key Agreement Technique Using FCM Under HC-IoT Architectures

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Computers, Materials & Continua 2022年第7期72卷 1373-1389页

作者： Chandrashekhar Meshram Agbotiname Lucky Imoize Sajjad Shaukat Jamal Parkash Tambare Adel R.Alharbi Iqtadar Hussain Department of Post Graduate Studies and Research in Mathematics Jayawanti Haksar Government Post-Graduation CollegeCollege of Chhindwara UniversityBetul460001M.P.India Department of Electrical and Electronics Engineering Faculty of EngineeringUniversity of LagosAkokaLagos100213Nigeria Department of Electrical Engineering and Information Technology Institute of Digital CommunicationRuhr University44801BochumGermany Department of Mathematics College of ScienceKing Khalid UniversityAbhaSaudi Arabia Water Resources&Applied Mathematics Research Lab Nagpur440027India College of Computing and Information Technology University of TabukTabuk71491Saudi Arabia Mathematics Program Department of MathematicsStatistics and PhysicsCollege of Arts and SciencesQatar University2713DohaQatar

The Human-Centered Internet of Things(HC-IoT)is fast becoming a hotbed of security and privacy *** users can establish a common session key through a trusted server over an open communication channel using a three-party authenticated key *** of the early authenticated key agreement systems relied on pairing,hashing,or modular exponentiation processes that are computationally intensive and *** order to address this problem,this paper offers a new three-party authenticated key agreement technique based on fractional chaotic *** new scheme uses fractional chaotic maps and supports the dynamic sensing of HC-IoT devices in the network architecture without a password *** projected security scheme utilized a hash function,which works well for the resource-limited HC-IoT *** results show that our new technique is resistant to password guessing attacks since it does not use a ***,our approach provides users with comprehensive privacy protection,ensuring that a user forgery attack causes no ***,our new technique offers better security features than the techniques currently available in the literature.

关键词： Three-party authenticated key agreement anonymity fractional chaotic maps Chebyshev polynomial password table human-centered internet of things(HC-IoT)

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A NovelMethod for Determining Tourism Carrying Capacity in a Decision-Making Context Using q−Rung Orthopair Fuzzy Hypersoft Environment

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Computer Modeling in engineering & Sciences 2024年第2期138卷 1951-1979页

作者： Salma Khan Muhammad Gulistan NasreenKausar Seifedine Kadry Jungeun Kim Department of Mathematics and Statistics Hazara UniversityMansehra21120Pakistan Department of Mathematics Faculty of Arts and SciencesYildiz Technical UniversityEsenlerIstanbul34210Turkey Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityP.O.Box 13-5053ByblosLebanon Department of Software and CMPSI Konju National UniversityCheonan31080Korea

Tourism is a popular activity that allows individuals to escape their daily routines and explore new destinations for various reasons,including leisure,pleasure,or business.A recent study has proposed a unique mathematical concept called a q−Rung orthopair fuzzy hypersoft set(q−ROFHS)to enhance the formal representation of human thought processes and evaluate tourism carrying *** approach can capture the imprecision and ambiguity often present in human *** the advanced mathematical tools in this field,the study has also incorporated the Einstein aggregation operator and score function into the q−ROFHS values to supportmultiattribute decision-making *** implementing this technique,effective plans can be developed for social and economic development while avoiding detrimental effects such as overcrowding or environmental damage caused by tourism.A case study of selected tourism carrying capacity will demonstrate the proposed methodology.

关键词： q−Rung orthopair fuzzy hypersoft set decision-making tourism carrying capacity aggregation operator

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