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Secure transmission of medical image using a wavelet interval type-2 TSK fuzzy brain-imitated neural network

Soft Computing 2025年第4期29卷 2311-2329页

作者： Pham, Duc-Hung Huynh, Tuan-Tu Lin, Chih-Min Giap, Van Nam Vu, Van-Phong Department of Electrical Engineering Yuan Ze University No. 135 Yuandong Road Zhongli 320 Taoyuan Taiwan Faculty of Electrical and Electronic Engineering Hung Yen University of Technology and Education Hai Duong160000 Viet Nam Faculty of Mechatronics and Electronics Lac Hong University No. 10 Huynh Van Nghe Road810000 Bien Hoa Dong Nai Viet Nam School of Electrical & Electronic Engineering Hanoi University of Science and Technology Hai Ba Trung Hanoi100000 Viet Nam Department of Automatic Control Ho Chi Minh City University of Technology and Education Ho Chi Minh70000 Viet Nam

The purpose of this research is to develop a new design of a wavelet interval type-2 takagi–Sugeno-Kang fuzzy brain-imitated neural network (WIT2TFBINN), which is a combination of the mathematical models of a Takagi–Sugeno-Kang (TSK) fuzzy system based on wavelet interval type-2 function (WIT2) and a wavelet interval type-2 fuzzy brain imitated neural network (FBINN). The proposed WIT2TFBINN is used for synchronization control of a 4D Lorentz chaotic system and has the benefits of wavelet interval type-2 membership function, TSK fuzzy inference system, decision making, and emotional activity. To provide fast training, the proposed method's parameter update laws are derived using the gradient descent method. The proposed WIT2TFBINN synchronization technique is then applied to the transmission of medical images in a secure manner. As a cipher image, a medical image is encrypted into a chaotic trajectory. After transmission, the image can be decrypted using chaotic trajectory synchronization on the received signal. The simulation results show that the proposed neural network and the encryption/decryption method are powerful and effective. The results of the static test analysis (histogram, attack analysis, image with noise, image with cropping) show that the encryption/decryption method provides high security. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Membership functions

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Linear Convergent Distributed Nash Equilibrium Seeking With Compression

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IEEE Transactions on automatic control 2025年第5期70卷 3316-3323页

作者： Chen, Xiaomeng Wu, Yuchi Yi, Xinlei Huang, Minyi Shi, Ling Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Shanghai University School of Mechatronic Engineering and Automation Shanghai200444 China College of Electronics and Information Engineering Tongji University Department of Control Science and Engineering Shanghai201804 China Massachusetts Institute of Technology Lab for Information & Decision Systems CambridgeMA02139 United States Carleton University School of Mathematics and Statistics OttawaONK1S 5B6 Canada

Information compression techniques are majorly employed to reduce communication cost over peer-to-peer links. In this article, we investigate distributed Nash equilibrium (NE) seeking problems in a class of noncooperative games over directed graphs with information compression. To improve communication efficiency, a compressed distributed NE seeking (C-DNES) algorithm is proposed to obtain an NE for games, where the differences between decision vectors and their estimates are compressed. The proposed algorithm is compatible with a general class of compressors, including both unbiased and biased compressors. Moreover, our approach only requires the adjacency matrix of the directed graph to be row-stochastic, in contrast to past works that relied on balancedness or specific global network parameters. It is shown that C-DNES not only inherits the advantages of conventional distributed NE algorithms, achieving linear convergence rate for games with restricted strongly monotone mappings, but also saves communication costs in terms of transmitted bits. Finally, numerical simulations illustrate the advantages of C-DNES in saving communication cost by an order of magnitude under different compressors. © 1963-2012 IEEE.

关键词： Stochastic systems

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A Fallback Localization Algorithm for Automated Vehicles Based on Object Detection and Tracking

IEEE Open Journal of Vehicular Technology

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IEEE Open Journal of Vehicular Technology 2025年 6卷 1085-1099页

作者： Rodriguez-Arozamena, Mario Matute, Jose Araluce, Javier Kuschnig, Lukas Pilz, Christoph Schratter, Markus Rastelli, Joshue Perez Zubizarreta, Asier TECNALIA Derio48160 Spain University of the Basque Country UPV/EHU Department of Automatic Control and Systems Engineering Bilbao48013 Spain Virginia Tech Transportation Institute 3500 Transportation Research Plaza BlacksburgVA24061 United States Virtual Vehicle Research GmbH Department of Electrics Electronics and Software Graz8010 Austria CEIT-Basque Research and Technology Alliance Donostia-San Sebastián San Sebastián20009 Spain

Integrating Automated Vehicles (AVs) into everyday traffic is an ongoing challenge. Ensuring the safety of all involved agents, even in the presence of system failures, is crucial, especially in urban environments. This paper introduces a fallback-oriented localization algorithm for AVs designed to operate during main localization source failures. The method leverages stationary vehicles as dynamic landmarks, identified through the perception module, despite their initially unknown positions. By tracking relative positions before failure and applying trilateration, the algorithm estimates the ego vehicle's position. The proposed algorithm is evaluated through simulations, a real-world dataset, and practical tests on two vehicle models. The results include an average trajectory error of 0.62 m and 1.58 deg compared to the ground truth over different fallback maneuvers. This translates into an average relative translational error of 1.65% and a relative rotational error of 0.05 deg/m, improving the performance of an IMU-based dead reckoning and, hence, providing localization for performing safe stop maneuvers. © 2020 IEEE.

关键词： Tracking (position)

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CONVERGENCE ANALYSIS OF LINEARIZED q PENALTY METHODS FOR NONCONVEX OPTIMIZATION WITH NONLINEAR EQUALITY CONSTRAINTS

arXiv

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arXiv 2025年

作者： El Bourkhissi, Lahcen Necoara, Ion Department of Automatic Control and Systems Engineering National University of Science and Technology Politehnica Bucharest Romania Department of Automatic Control and Systems Engineering National University of Science and Technology Politehnica Bucharest Bucharest060042 Romania Gheorghe Mihoc-Caius Iacob Institute of Mathematical Statistics and Applied Mathematics of the Romanian Academy Bucharest050711 Romania

In this paper, we consider nonconvex optimization problems with nonlinear equality constraints. We assume that the objective function and the functional constraints are locally smooth. To solve this problem, we introduce a linearized q penalty based method, where q ∈ (1, 2] is the parameter defining the norm used in the construction of the penalty function. Our method involves linearizing the objective function and functional constraints in a Gauss-Newton fashion at the current iteration in the penalty formulation and introduces a quadratic regularization. This approach yields an easily solvable subproblem, whose solution becomes the next iterate. By using a novel dynamic rule for the choice of the regularization parameter, we establish that the iterates of our method converge to an ǫ-first-order solution in O(1/ǫ2+(q−1)/q) outer iterations. Finally, we put theory into practice and evaluate the performance of the proposed algorithm by making numerical comparisons with existing methods from literature. © 2025, CC BY.

关键词： Numerical methods

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Periodic-Enhanced Informer Model for Short-Term Wind Power Forecasting Using SCADA Data

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IEEE Transactions on Sustainable Energy 2025年

作者： Liu, Zhao-Hua Li, Long-Wei Wei, Hua-Liang Li, Ming Lv, Ming-Yang Zhang, Ying-Jie Hunan University of Science and Technology School of Information and Electrical Engineering Xiangtan411201 China University of Sheffield Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

Supervisory control and Data Acquisition (SCADA) systems can collect abundant information about wind farm operation and environment. To better make use of SCADA data, a periodic-enhanced informer model for short-term wind power forecasting using scada data is proposed. Firstly, to effectively filter out noise in SCADA data, a v-p curve-based method is adopted by incorporating a quartile approach to remove sparse outliers;a density-based spatial clustering of applications with noise (DBSCAN) algorithm is then employed to eliminate stacked outliers from the power curve. Secondly, a multi-feature input set selection method based on Maximization Information Coefficient is introduced to make better use of the SCADA system data by reducing the number of features. Thirdly, a Temporal Convolutional Network (TCN) is designed to extract the scalar projection of the input set, followed by fusing the local time stamp and global time stamp to build the periodic information enhanced prediction model embedding layer. Subsequently, the enhanced input set is fed into an informer model to predict future wind power. Finally, considering the multiple temporal scales structure characteristics present in the dataset, a multi-scale deep fusion module is established in the informer model to deeply integrate the features of different scales. It can simultaneously avoid the resource waste and overfitting problems caused by increasing the network depth. The experimental results, which are obtained from several deep learning methods on real SCADA data, demonstrate that the suggested approach has good predictive capability. © 2010-2012 IEEE.

关键词： Wind forecasting

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Optimal Design and Tuning of TID Power System Stabilizer for Multi-machine Power System Using Dandelion Optimizer

Optimal Design and Tuning of TID Power System Stabilizer for...

引用

International Conference on Artificial Intelligence in Renewable Energetic systems, IC-AIRES 2024

作者： Hattabi, Intissar Kheldoun, Aissa Bradai, Rafik Khettab, Soufian SET Laboratory Blida 1 University Faculty of Technology Electrical and Control Department Blida Algeria Laboratory of Signals and Systems Institute of Electrical and Electronic Engineering University M’hamed Bougara Boumerdes35000 Algeria LATSI Blida 1 University Faculty of Technology Electronics Department Blida09000 Algeria

ISBN: (纸本)9783031803000

In this study, we explore the enhancement of stability in a four-machine power system through the optimized tuning of Tilt-Integral-Derivative Power System Stabilizer (TID-PSS) parameters. We introduce a novel application of the Dandelion Optimizer (DO) algorithm, designed to provide robust damping and stability under fault conditions. To improve the effectiveness of the DO algorithm, we compare its performance with several established optimization techniques, including Particle Swarm Optimization (PSO), HGS (Hunger Games Search), HBA (Honey Badger Algorithm), EO (Equilibrium Optimizer), and AVOA (African Vulture Optimization algorithm. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Particle swarm optimization (PSO)

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Enhancing Thin-Film Wafer Inspection With A Multi-Sensor Array And Robot Constraint Maintenance

arXiv

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arXiv 2025年

作者： Sánchez-Arriaga, Néstor Eduardo Canzini, Ethan Espley-Plumb, Nathan John Farnsworth, Michael Pope, Simon Leyland, Adrian Tiwari, Ashutosh Department of Automatic Control & Systems Engineering University of Sheffield Sheffield United Kingdom Department of Materials Science & Engineering University of Sheffield Sheffield United Kingdom Airbus Robotics Airbus UK Chester United Kingdom

Thin-film inspection on large-area substrates in coating manufacture remains a critical parameter to ensure product quality;however, extending the inspection process precisely over a large area presents major challenges, due to the limitations of the available inspection equipment. An additional manipulation problem arises when automating the inspection process, as the silicon wafer requires movement constraints to ensure accurate measurements and to prevent damage. Furthermore, there are other increasingly important large-area industrial applications, such as Roll-to-Roll (R2R) manufacturing where coating thickness inspection introduces additional challenges. This paper presents an autonomous inspection system using a robotic manipulator with a novel learned constraint manifold to control a wafer to its calibration point, and a novel multi-sensor array with high potential for scalability into large substrate areas. We demonstrate that the manipulator can perform required motions whilst adhering to movement constraints. We further demonstrate that the sensor array can perform thickness measurements statically with an error of 0.5◦ from the calibration point whilst monitoring the RMSE and R2 over 1406 data points. These features are potentially useful for detecting displacement variations in R2R manufacturing processes. © 2025, CC BY-NC-ND.

关键词： Silicon wafers

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An ANN-Enhanced Approach for Flatness-Based Constrained control of Nonlinear systems

arXiv

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arXiv 2025年

作者： Do, Huu-Thinh Prodan, Ionela Stoican, Florin Univ. Grenoble Alpes Grenoble INP LCIS ValenceF-26000 France Institute of Engineering and Management University Grenoble Alpes France Department of Automatic Control and Systems Engineering UPB Romania

Neural networks have proven practical for a synergistic combination of advanced control techniques. This work analyzes the implementation of rectified linear unit neural networks to achieve constrained control in differentially flat systems. Specifically, the class of flat systems enjoys the benefit of feedback linearizability, i.e., the systems can be linearized by means of a proper variable transformation. However, the price for linearizing the dynamics is that the constraint descriptions are distorted geometrically. Our results show that, by using neural networks, these constraints can be represented as a union of polytopes, enabling the use of mixed-integer programming tools to guarantee constraint satisfaction. We further analyze the integration of the characterization into efficient settings such as control Lyapunov function-based and model predictive control (MPC). Interestingly, this description also allows us to explicitly compute the solution of the MPC problem for the nonlinear system. Several examples are provided to illustrate the effectiveness of our framework. © 2025, CC BY-NC-ND.

关键词： Mixed-integer linear programming

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Hybrid Oscillation Damping and Inertia Management for Distributed Energy Resources

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IEEE Transactions on Power systems 2025年

作者： Feng, Cheng Huang, Linbin He, Xiuqiang Wang, Yi Dorfler, Florian Kang, Chongqing Tsinghua University Department of Electrical Engineering Beijing100084 China Zhejiang University College of Electrical Engineering Hangzhou310027 China Tsinghua University Department of Automation Beijing100084 China The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong Automatic Control Laboratory ETH Zurich8092 Switzerland

Power systems dominated by converter-interfaced distributed energy resources (DERs) typically exhibit weaker damping capabilities and lower inertia, compromising system stability. Although individual DER controllers are evolving to provide superior oscillation damping capabilities and inertia supports, there is a lack of network-wide coordinated management measures for multiple DERs, potentially leading to unexpected instability and cost-effectiveness problems. To address this gap, this paper introduces a hybrid oscillation damping and inertia management strategy for multiple DERs, considering network coupling effects, and seeks to encourage DERs to provide enhanced damping and inertia with appropriate economic incentives. We first formulate an optimization problem to tune and allocate damping and inertia coefficients for DERs, minimizing associated power and energy costs while ensuring hard constraints for system frequency stability and small-signal stability. The problem is built upon a novel convex parametric formulation that integrates oscillation mode location and frequency trajectory requirements, equipped with a theoretical guarantee, and eliminating the need for iterative tuning and computation burdens. Furthermore, to increase the willingness of DERs to cooperate, we further design appropriate economic incentives to compensate for DERs' costs based on the proposed cost minimization problem, and assess its impact on system cost-efficiency. Numerical tests highlight the effectiveness of the proposed method in promoting system stability and offer insights into potential economic benefits. © 1969-2012 IEEE.

关键词： Damping

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Immersive Virtual Reality Cruise Garden Relaxation Train: A Novel BCI Platform for Depression 6th

Immersive Virtual Reality Cruise Garden Relaxation Train: A ...

引用

6th International Conference on Biomedical and Health Informatics, ICBHI 2024

作者： Tsai, Shu-Hui Chao, Chih-Chiang Lin, Yue-Der Kuo, Chin-Jung Dong, Shu-Ping Liu, Chun-Yuan Ko, Hsin-Yi Wang, Jenn-Hann Liu, Ching Hsiu Service Systems Technology Center Industrial Technology Research Institute Hsin-Chu Taiwan Center for Measurement Standards Industrial Technology Research Institute Hsin-Chu Taiwan Department of Automatic Control Engineering Feng-Chia University Taichung Taiwan Central Division Industrial Technology Research Institute Hsin-Chu Taiwan Mackay Memorial Hospital Hsin-Chu Taiwan

ISBN: (纸本)9783031863226

The staggering global prevalence of depression extends to Taiwan, where approximately 8.9% of the population exhibits symptoms of depression, affecting around 2 million individuals, with 1.25 million classified as severe cases. Depression exerts profound impacts on both socioeconomic and health aspects, ranking as one of the leading causes of disability and closely linked with dementia. Over 70% of suicides involve individuals with depression, and those with depression face significantly higher suicide risks compared to the general population. Of particular concern is the higher-than-expected prevalence of depression, which, if left untreated, may escalate suicide risks. Research indicates that individuals with depression display unstable and imbalanced brainwave patterns, characterized by a lack of normal baseline brainwaves and elevated slow waves. While the commonly used repeated transcranial magnetic stimulation (rTMS) therapy can modulate brainwaves, the treatment is costly and often associated with high relapse rates, imposing a significant burden on patients. Recent studies reveal that potential depression sufferers commonly experience brainwave dysregulation and neuro-immune disruption, emphasizing the need for personalized emotional, brainwave, and autonomic nervous system regulation, along with community support. This study aims to integrate elements such as "Brainwave Entrainment with Schumann Resonance and Autonomic Nervous System Feedback Device", "Light Therapy Device", and "Garden Train-Style Anxiety-Relief Environment" to provide simulated natural environment brainwave and autonomic nervous system regulation, as well as light therapy. Additionally, it involves designing a safe "Garden Train" virtual reality environment to offer a secure and comforting anxiety-relief space for individuals at risk or suffering from depression, as well as caregivers. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electrotherapeutics

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