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18th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2022

作者： Liu, Xiaodong Guo, Haipeng Liu, Huanyu Department of Automatic Test and Control School of Electronics and Information Engineering Harbin Institute of Technology Harbin150080 China Information Countermeasure Technique Institute School of Computer Science and Technology Harbin Institute of Technology Harbin150080 China

ISBN: (纸本)9789819901043

High-resolution magnetic resonance imaging (MRI) is very important for doctors to judge the condition. At present, the depth learning method has been widely studied in the super-resolution reconstruction of MR images. However, due to the distribution difference between the real low-resolution MR images and the down sampled MR images used to train the super-resolution model under experimental conditions, the super-resolution performance of the super-resolution method is often seriously degraded when super-resolution is applied to the real low-resolution MR images. Blind super-resolution methods used to solve such problems in natural image super-resolution are lack of image content constraints. It is not advisable to introduce additional information that will interfere with doctors’ diagnosis in the super-resolution process of MR images. These two problems hinder the application of super-resolution method in real MR images. In order to solve these problems, an unpaired MR image super-resolution network based on domain migration is proposed. The proposed DMN can use a real set of low to high-resolution unpaired MR images as input, migrate the real low-resolution MR images to the down sampled MR image domain, and then super-resolution reconstruction is carried out through the paired super-resolution network obtained through supervision training. Experiments on fast MRI datasets verify the effectiveness of this method in solving the super-resolution problem of real MR images. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Magnetic resonance imaging

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Dehazing Quality Evaluation Algorithm Integrating Dark Channel Theory and Image Depth Estimation

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第2期9卷 1119-1133页

作者： Huang, Jia-Yan Xu, Xin-Xin Luo, Shao-Ye Zhao, Wan-Qi Fan, Hao-Yi Grau, Antoni Guerra, Edmundo Wang, Chuan-Sheng Zhang, Fu-Quan New Engineering Industry College Putian University Putian351100 China The University of Sheffield SheffieldS10 2TN United Kingdom School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450001 China Department of Automatic Control Technical Polytechnic University of Catalonia Barcelona08034 Spain Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350108 China

Image dehazing is an important research topic and hotspot in the fields of image processing and computer vision. Therefore, evaluating the performance of image dehazing algorithms has become an import research issue. However, current image quality assessment (IQA) algorithms only judge the final dehazed image and do not give an analysis of the capabilities of the image dehazing algorithm. In order to fill this gap, we propose an image dehazing quality assessment method that integrated the dark channel prior with image depth estimation. The proposed method first uses the dark channel prior to estimate the absolute amount of haze removed from each pixel as an indication of the performance of dehazing. Then, a dual-scale variance ratio method is introduced to estimate the scene depth of the image. Finally, the relative dehazing is calculated from the obtained absolute dehazing and the image depth to represent the capability of the dehazing algorithm. The method proposed in this paper first introduces the concepts of relative and absolute dehazing quantities, along with pioneering a pixel-level evaluation of dehazing outcomes. Extensive experiments show that the algorithm proposed in this paper is more in line with human subjective judgment than other IQA algorithms. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Pixels

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DISTANCE BETWEEN SPECIES BY CONFUSION OPERATORS OF MULTI-CLASS CLASSIFIERS

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2025年第1期2025卷 171-182页

作者： Tarbă, Nicolae Irimescu, Ionela N. Pleavă, Ana M. Scarlat, Eugen N. Mihăilescu, Mona Doctoral School Computer Science and Engineering Department Faculty of Automatic Control and Computers National University of Science and Technology POLITEHNICA Bucharest Romania Applied Sciences Doctoral School National University of Science and Technology POLITEHNICA Bucharest Romania CAMPUS Research Center National University of Science and Technology POLITEHNICA Bucharest Romania Physics Dept National University of Science and Technology POLITEHNICA Bucharest Romania Physics Dept Research Center for Applied Sciences in Engineering National University of Science and Technology POLITEHNICA Bucharest Romania

We introduce a method to evaluate the similarities between classes of objects based on the confusion matrices coming from the multi-class machine learning (ML) predictors that operate in the vector space generated by the classes. Operation in the confusion domain allows us to use the distance as evaluation metric of the closeness of the relevant properties carried by the classes. We applied the method to semiquantitatively evaluate the effects of X-ray and carbon ion radiation on cultured SW1353 chondrosarcoma cells with respect to the non-irradiated reference. We prove that it is possible to draw useful information on the strength of cells nuclei changes. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Radiation effects

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Contribution to the Sensors Faults Diagnosis of a Variable Speed Wind Turbine: Multimodel Approach

Contribution to the Sensors Faults Diagnosis of a Variable S...

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International Conference on Systems and control (ICSC)

作者： Yosra Sayahi Moez Allouche Mariem Ghamgui Sandrine Moreau Driss Mehdi Laboratory of Sciences and Techniques of Automatic Control & Computer Engineering (Lab-STA) National Engineering School of Sfax University of Sfax Sfax Tunisia Laboratory of Computer Science and Automatic Control for Systems University of Poitiers France

This paper highlights the importance of using a Doubly-Fed Induction Generators (DFIG) in the wind industry due to their ability to adapting for all variations in wind speed, thus providing increased efficiency and reliability. However, like any machine, DFIG are not immune to dysfunctional problems and faults (sensor faults, actuator faults and system faults) which affect energy production. To remedy this problem, we develop a Fault Detection and Insolation (FDI) system for sensors fault diagnosis in wind turbine. This work specifically addresses the use of observer's bench to detect and locate faults, such as intermittent sensor faults, inter-coil short circuits, emphasizing a multi-model approach. We use the Dedicated Observer Structure (DOS) and the Generalized Observer Structure (GOS) to solve the complex challenge of multiple and simultaneous sensor fault. Simulation results are presented to assess the effectiveness of the proposed diagnostic methods.

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Designing Quantum-Resilient Blockchain Frameworks: Enhancing Transactional Security with Quantum Algorithms in Decentralized Ledgers

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International Journal of Advanced computer Science and Applications 2025年第4期16卷 618-628页

作者： Kale, Meenal R. Baker El-Ebiary, Yousef A. Sathiya, L. Burugari, Vijay Kumar Yulduz, Erkiniy Muniyandy, Elangovan Alanazi, Rakan Department of Humanities Yeshwantrao Chavan College of Engineering Hingna Nagpur India Faculty of Informatics and Computing UniSZA University Malaysia Department of CSE Panimalar Engineering College Chennai India Dept of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Automatic Control and Computer Engineering Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Department of Information Technology Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia

Quantum computing is progressing at a fast rate and there is a real threat that classical cryptographic methods can be compromised and therefore impact the security of blockchain networks. All of the ways used to secure blockchain like Rivest–Shamir–Adleman (RSA), Elliptic Curve Cryptography (ECC) and Secure Hash Algorithm 256-bit (SHA256) are the characteristic of the traditional cryptographic techniques vulnerable to attack by quantum algorithms: Shor’s and Grover’s algorithms: can efficiently break asymmetric encryption and speed up brute force attacks. Because of this vulnerability, there exists a need to develop an advance quantum resilient blockchain framework to protect the decentralized ledgers from the future threats of the quantum. This research proposes Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD) and Quantum Random Number Generation (QRNG) as a formidable architectural integration, to fortify security of blockchain. Classical encryption is replaced with PQC, QKD with secure key exchange by detecting eavesdropping, and QRNG with improving cryptographic randomness to remove the predictable key vulnerability. Only with a small loss of transaction efficiency, we increase transaction encryption accuracy, key exchange security, and resistance to quantum attacks. In this quantum enhanced blockchain design, the idea is to preserve the decentralization, transparency and security and at the same time overcome the future quantum threat. By going through rigorous analysis and comparative evaluation, we demonstrate that the approach saves blockchain networks from the emerging quantum risks to make sure that the decentralized finance, smart contracts and cross chain transactions. © (2025), (Science and Information Organization). All rights reserved.

关键词： Quantum cryptography

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Quality Assessment of Electrocardiogram Signals Using Contrastive Learning 13

Quality Assessment of Electrocardiogram Signals Using Contra...

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13th International Conference on Information Technology in Medicine and Education, ITME 2023

作者： Zhong, Meiping Li, Zuoyong Wang, ChuanSheng Huang, Jiayan Zhao, WanQi Fan, Haoyi College of Computer and Big Data Fuzhou University Fuzhou350108 China College of Computer and Control Engineering Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou350121 China Polytechnic University of Catalonia Department of Automatic Control Technical Barcelona08034 Spain New Engineering Industry College Putian University Putian351100 China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450001 China

ISBN: (纸本)9798350319156

Quality assessment of electrocardiogram (ECG) signals holds significant importance in reducing the high-frequency false alarms in automated arrhythmia diagnosis. Existing quality assessment methods primarily rely on empirical threshold setting or the calculation of relevant statistical values to formulate decision rules. However, the thresholds set in this manner are typically based on experience and knowledge, resulting in decision rules that lack flexibility and struggle to adapt to changes in the dataset. To address this issue, many researchers have turned to deep learning algorithms for ECG signal quality assessment. Nevertheless, most existing deep learning methods usually require large-scale and labeled ECG data for training, whose acquiring is typically challenging. Therefore, this paper proposes an efficient method for ECG signal quality assessment. First, we use a contrast learning framework to construct sample pairs through multilevel data augmentation. Second, a densely connected convolutional network (DenseNet) is used to improve the model's ability of extracting local features in ECG signals, and a Squeeze-and-Excitation attention module is introduced to make the model pay more attention to critical features. Finally, the feature representation is learned by minimizing the contextual contrast loss to bring similar samples closer and push dissimilar samples farther away. In this way, we can utilize the contrastive learning framework to extract temporal representations from unlabeled data and transfer them to downstream ECG signal quality assessment tasks. Extensive experiments on the Physionet/CinC Challenge 2011 dataset demonstrate that our approach outperforms most existing quality assessment methods. © 2023 IEEE.

关键词： Electrocardiograms

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On the Stabilization of a Class of OSL Conformable Fractional Order Nonlinear Systems 11

On the Stabilization of a Class of OSL Conformable Fractiona...

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11th IEEE International Conference on Systems and control, ICSC 2023

作者： Alsharif, Ali Omar M. Jmal, A. Naifar, O. Ahmed, Ausama H. Gassara, Hamdi Makhlouf, A. Ben Electrical Engineering Department BP 1173 Sfax3038 Tunisia Elmergib University Faculty of Engineering Department of Electrical & Computer Engineering Alkhums Libya National School of Engineering of Sfax University of Sfax Laboratory of Sciences and Techniques of Automatic Control and Computer Engineering Tunisia Faculty of Sciences of Sfax Department of Mathematics Route Soukra BP 1171 Sfax3000 Tunisia

ISBN: (纸本)9798350304886

One-Sided Lipschitz (OSL) fractional order modeling is a top choice for solving the stabilization issue of nonlinear systems. Despite numerous studies on the subject, there remains a gap in understanding when it comes to fractional calculus. By providing a stabilizing strategy for a certain type of OSL fractional order nonlinear systems, this study fills the gap. A numerical example demonstrating the correctness of the suggested approach and demonstrating its efficacy for the tested class. © 2023 IEEE.

关键词： Stabilization

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Output Tracking control of a Nonlinear System Based on Takagi-Sugeno Fuzzy Model: Generalized Partial Eigenstructure Assignment Approach

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IEEE Access 2024年 12卷 18520-18535页

作者： Soltanian, Farzad Shasadeghi, Mokhtar Mobayen, Saleh Skruch, Pawel University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2M9 Canada Shiraz University of Technology Electrical Engineering Faculty Shiraz71557-13876 Iran National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan AGH University of Science and Technology Department of Automatic Control and Robotics Kraków30-059 Poland

This paper introduces an innovative optimal control approach to achieve output tracking while incorporating H2-performance specifications in a specific class of nonlinear dynamics modeled by the Takagi-Sugeno fuzzy model (TSFM). The primary innovation lies in extending partial eigenstructure assignment to TSFM-based nonlinear systems within the framework of sliding mode control (SMC). We propose a two-step methodology for designing optimal sliding surface gains. Initially, optimal state feedback gains are computed for each rule consequence containing a linear subsystem, adhering to predetermined eigenvalues and satisfying H2-performance criteria. Subsequently, using a convex combination, the overall state feedback gain is calculated and utilized to design sliding matrix gains. The sliding matrix gains are then determined by strategically combining previously calculated state-feedback gains in a convex optimization problem. We reframe the output tracking strategy as a stabilization problem using a virtual control input and reformulate the optimization task concerning tracking state errors. This process yields state feedback gains, sliding gains, and the formulation of the virtual control input. The effectiveness of our approach is verified through comprehensive simulations, emphasizing its capability in addressing output tracking challenges within nonlinear systems. © 2013 IEEE.

关键词： Transient analysis

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Stress Matrix-Based Formation control of Multi-Agent Systems with Discrete-Time Communication 12

Stress Matrix-Based Formation Control of Multi-Agent Systems...

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2024 12th International Conference on Systems and control, ICSC 2024

作者： Onuoha, Okechi Ubochi, Brendan Chijioke Kurawa, Suleiman Tang, Zezhi Wu, Changshun Dong, Yi University of Hertfordshire United Kingdom Federal University of Technology The Department of Electrical and Electronic Engineering Akure Nigeria University of Manchester The Department of Electrical Engineering United Kingdom University of Sheffield The Department of Automatic Control and Systems Engineering United Kingdom University Grenoble Alpes Grenoble38000 France The School of Electronics & Computer Science University of Southampton United Kingdom

ISBN: (纸本)9798331531812

This study addresses the affine formation maneuver control of cooperative multi-agent systems (MAS) having periodic inter-agent communication for both static and dynamic leader cases. Here, we focus on the leader-follower MASs. The primary aim of the control system is to steer the entire collection of agents to produce required patterns (geometric) along with any required maneuver through the direct control of only few a selected agents referred to as leaders. Most of the existing works are constrained to either the individual agents communicate with each other in continuous-time or the sample-data scenario where the leaders are stationary or have constant acceleration or velocities. Here, we consider the scenarios where the velocities of the leaders can be time-varying or constant. Here, different cases are addressed and some control laws are proposed. Conditions are established to help guarantee the overall stability of the systems. A simulation study is employed for the illustration of our proposed laws. © 2024 IEEE.

关键词： Continuous time systems

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Finite-time robust performance improvement for non-linear systems in the presence of input non-linearity and external disturbance

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IET control Theory and Applications 2023年第6期17卷 672-682页

作者： Wang, Haoping Ghaffari, Valiollah Mobayen, Saleh Bartoszewicz, Andrzej Fekih, Afef Assawinchaichote, Wudhichai School of Automation Nanjing University of Science and Technology Nanjing China Department of Electrical Engineering Persian Gulf University Bushehr Iran Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Yunlin Douliou Taiwan Institute of Automatic Control Lodz University of Technology Lodz Poland Department of Electrical and Computer Engineering University of Louisiana at Lafayette LafayetteLA United States Department of Electronic and Telecommunication Engineering Faculty of Engineering King Mongkut's University of Technology Thonburi Bangkok Thailand

The main focus of this research is on improving the performance of dynamic systems with actuator non-linearities and time-varying disturbances. To this end, using the concept of finite-time stability, a novel observer is presented to estimate the disturbances in systems with input non-linearities. Then, introducing an innovative sliding manifold, a robust observer-based technique is derived to guarantee the finite-time stability of the sliding surface. Hence, the closed-loop system would be stable in the presence of hard non-linearities and time-varying disturbances. The performance of the proposed approach was assessed in using a simulation study of two numerical examples. The obtained results and comparison with a conventional method confirmed the benefits of the suggested approach. © 2022 The Authors. IET control Theory & Applications published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Closed loop systems

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