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Stability Analysis of Fuzzy Logic Control Systems for a Class of Nonlinear SISO Discrete-Time Systems

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IFAC Proceedings Volumes 2011年第1期44卷 13612-13617页

作者： Radu-Emil Precup Marius-Lucian Tomescu Stefan Preitl Emil M. Petriu Claudia-Adina Dragoş Department of Automation and Applied Informatics “Politehnica” University of Timisoara Bd. V. Parvan 2 RO-300223 Timisoara Romania (Tel: +40-256403229 Computer Science Faculty “Aurel Vlaicu” University of Arad Complex Universitar M Str. Elena Dragoi 2 RO-310330 Arad Romania Department of Automation and Applied Informatics “Politehnica” University of Timisoara Bd. V. Parvan 2 RO-300223 Timisoara Romania School of Information Technology and Engineering University of Ottawa 800 King Edward Ottawa Ontario K1N 6N5 Canada

Abstract This paper suggests a new stability analysis theorem dedicated to a class of fuzzy logic control systems. The fuzzy logic control systems consist of nonlinear Single Input-Single Output (SISO) discrete-time processes controlled by Takagi-Sugeno fuzzy logic controllers. The stability analysis is conducted on the basis of Lyapunov's direct method with quadratic Lyapunov function candidates. The theorem proves that if the derivative of the Lyapunov function candidate is negative definite in the active region of each fuzzy rule then the fuzzy logic control system will be asymptotically stable in the sense of Lyapunov. Sufficient stability conditions are offered. An example related to the design of a stable fuzzy logic control system for the discrete-time Lorenz chaotic system and simulation results are included.

关键词： Discrete-time systems fuzzy logic control Lyapunov function candidates nonlinear systems stability analysis

Generalized Implicit Neural Representation for Efficient MRI Parallel Imaging Reconstruction

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arXiv 2023年

作者： Li, Hao Zhou, Yusheng Liu, Jianan Liu, Xiling Huang, Tao Lyu, Zhihan Cai, Weidong Chen, Wei Department of Neuroradiology University Hospital Heidelberg Heidelberg Germany School of Electrical Engineering and Automation Wuhan University Wuhan China Momoni AI Gothenburg Sweden Department of Stomatology Shenzhen People's Hospital Shenzhen China College of Science and Engineering James Cook University SmithfieldQLD4878 Australia Department of Game Design Faculty of Arts Uppsala University Sweden School of Computer Science University of Sydney Sydney Australia School of Biomedical Engineering The University of Sydney Camperdown Australia

High-resolution magnetic resonance imaging (MRI) is essential in clinical diagnosis. However, its long acquisition time remains a critical issue. Parallel imaging (PI) is a common approach to reduce acquisition time by periodically skipping specific k-space lines and reconstructing images from undersampled data. This study presents a generalized implicit neural representation (INR)- based framework for MRI PI reconstruction, addressing limitations commonly encountered in conventional methods, such as subject-specific or undersampling scale-specific requirements and long reconstruction time. The proposed method overcomes these limitations by leveraging prior knowledge of voxel-specific features and integrating a novel scale-embedded encoder module. This encoder generates scale-independent voxel-specific features from undersampled images, enabling robust reconstruction across various undersampling scales without requiring retraining for each specific scale or subject. The framework's INR model treats fully sampled MR images as a continuous function of spatial coordinates and prior voxel-specific features, efficiently reconstructing high-quality MR images from undersampled data. Extensive experiments on publicly available MRI datasets demonstrate the superior performance of the proposed method in reconstructing images at multiple acceleration factors (4×, 5×, and 6×), achieving higher evaluation metrics and visual fidelity compared to stateof- the-art methods. In terms of efficiency, this INR-based approach exhibits notable advantages, including reduced floating point operations and GPU usage, allowing for accelerated processing times while maintaining high reconstruction quality. The generalized design of the model significantly reduces computational resources and time consumption, making it more suitable for real-time clinical applications. The proposed framework represents a promising solution for faster and more flexible MRI acquisition, capable of enhancing the cl

关键词： Magnetic resonance imaging

The learning of fuzzy cognitive maps with noisy data: A rapid and robust learning method with maximum entropy

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arXiv 2019年

作者： Feng, Guoliang Lu, Wei Pedrycz, Witold Yang, Jianhua Liu, Xiaodong School of Control Science and Engineering Dalian University of Technology Dalian116023 China School of Automation Engineering Northeast Electric Power University Jilin132012 China School of Control Science and Engineering Dalian University of Technology Dalian116023 China Department of Electrical and Computer Engineering University of Alberta EdmontonABT6R 2V4 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia

Numerous learning methods for fuzzy cognitive maps (FCMs), such as the Hebbian-based and the population-based learning methods, have been developed for modeling and simulating dynamic systems. However, these methods are faced with several obvious limitations. Most of these models are extremely time consuming when learning the large-scale FCMs with hundreds of nodes. Furthermore, the FCMs learned by those algorithms lack robustness when the experimental data contain noise. In addition, reasonable distribution of the weights is rarely considered in these algorithms, which could result in the reduction of the performance of the resulting FCM. In this article, a straightforward, rapid, and robust learning method is proposed to learn FCMs from noisy data, especially, to learn large-scale FCMs. The crux of the proposed algorithm is to equivalently transform the learning problem of FCMs to a classic-constrained convex optimization problem in which the least-squares term ensures the robustness of the well-learned FCM and the maximum entropy term regularizes the distribution of the weights of the well-learned FCM. A series of experiments covering two frequently used activation functions (the sigmoid and hyperbolic tangent functions) are performed on both synthetic datasets with noise and real-world datasets. The experimental results show that the proposed method is rapid and robust against data containing noise and that the well-learned weights have better distribution. In addition, the FCMs learned by the proposed method also exhibit superior performance in comparison with the existing methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Fuzzy rules

Medical Visual Prompting (MVP): A Unified Framework for Versatile and High-Quality Medical Image Segmentation

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Medical Visual Prompting (MVP): A Unified Framework for Vers...

IEEE International Conference on Systems, Man and Cybernetics

作者： Yulin Chen Guoheng Huang Kai Huang Zijin Lin Guo Zhong Shenghong Luo Jie Deng Jian Zhou School of Automation Guangdong University of Technology Guangzhou China School of Computer Science and Technology Guangdong University of Technology Guangzhou China School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou China Faculty of Science and Technology University of Macau Macao China Department of Otorhinolaryngology The First Affiliated Hospital of Sun Yat-Sen University Guangzhou China Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Sun Yat-Sen University Cancer Center Guangzhou China South China Hospital Shenzhen University Shenzhen China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Accurate segmentation of lesion regions is crucial for clinical diagnosis and treatment across various diseases. While deep convolutional networks have achieved satisfactory results in medical image segmentation, they face challenges such as the loss of lesion shape information due to continuous convolution and downsampling, as well as the high cost of manually labeling lesions with varying shapes and sizes. To address these issues, we propose a novel Medical Visual Prompting (MVP) framework that leverages pre-training and prompting concepts from Natural Language Processing (NLP). The framework utilizes three key components: Super-Pixel Guided Prompting (SPGP) for superpixelating the input image, Image Embedding Guided Prompting (IEGP) for freezing patch embedding and merging with superpixels to provide visual prompts, and Adaptive Attention Mechanism Guided Prompting (AAGP) for pinpointing prompt content and efficiently adapting all layers. By integrating SPGP, IEGP, and AAGP, the MVP framework enables the segmentation network to better learn shape prompting information and facilitates mutual learning across different tasks. Extensive experiments conducted on five datasets demonstrate the superior performance of the proposed method in various challenging medical image tasks while simplifying single-task medical segmentation models. This novel framework offers improved performance with fewer parameters and holds significant potential for accurate segmentation of lesion regions in various medical tasks, making it clinically valuable.

关键词： Image segmentation Visualization Accuracy Shape Merging Multitasking Natural language processing Lesions Labeling Medical diagnostic imaging

Analysis of the EMF Parameters of the Stator Winding in the Run-Down Mode for Diagnosing Faults in an Induction Motor

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Analysis of the EMF Parameters of the Stator Winding in the ...

Smart Information Systems and Technologies (SIST), IEEE International Conference on

作者： Assel Yussupova Alexandra Potapenko Alexandr Neftissov Dariya Urazalimova Oleh Serbin Department of Electrical Engineering and Automation Toraighyrov University Pavlodar Kazakhstan Faculty of Computer Science Toraighyrov University Pavlodar Kazakhstan Research and innovation center “Industry 4.0” Astana IT University Astana Kazakhstan Dr. Habil. (Social sciences Communication) Chief Executive Officer CEO & Executive Director Yaroslav Mudryi National Library of Ukraine Professor of Kyiv National University of Culture and Arts Ukraine

In industry, as well as in power plants and substations of developed countries, induction motor (IM) are the most common electrical machine. They consume a large amount of the generated electricity. At the same time, they account for a sufficient number of failures of all electrical equipment. In this case, the proportion of failures due to a break in a squirrel-cage rotor, according to various authors, can reach about 5-10%. However, experimental studies at a number of plants and power plants in the Pavlodar region have shown that the damage factor of the squirrel-cage IM rotor is much higher. As a rule, the breakage of the squirrel-cage rotor rods does not lead to an immediate failure of the IM, but is accompanied by a deterioration in its electromechanical characteristics. Basically, the harm from the operation of such IM is expressed in increased energy consumption. Moreover, a year of its operation leads to an excessive consumption of electricity, comparable to the cost of the IM itself. The article shows that today there are a large number of proposals for a method for diagnosing damage to the short-circuited rotor winding of an induction motor (IM) during operation. However, their sensitivity is limited by the need for detuning from fluctuations in the parameters of the electrical network, vibrations of the IM and the uneven moment of resistance of its load. Most of these effects can be eliminated if IM diagnostics are carried out based on the analysis of the EMF parameters in the stator winding in the rundown mode. The authors have analyzed the parameters of the EMF of the stator winding at all stages of the rundown mode and substantiated the use of the presented method for diagnosing faults in an induction motor.

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Uncertainty-guided Boundary Learning for Imbalanced Social Event Detection

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arXiv 2023年

作者： Ren, Jiaqian Peng, Hao Jiang, Lei Liu, Zhiwei Wu, Jia Yu, Zhengtao Yu, Philip S. The Institute of Information Engineering Chinese Academy of Sciences the School of Cyber Security University of Chinese Academy of Sciences Beijing China The School of Cyber Science and Technology Beihang University Beijing1000191 China Salesforce AI Research CA94301 United States The School of Computing Macquarie University Sydney Australia The Faculty of Information Engineering and Automation Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Kunming650500 China The Department of Computer Science University of Illinois at Chicago ChicagoIL60607 United States

Real-world social events typically exhibit a severe class-imbalance distribution, which makes the trained detection model encounter a serious generalization challenge. Most studies solve this problem from the frequency perspective and emphasize the representation or classifier learning for tail classes. While in our observation, compared to the rarity of classes, the calibrated uncertainty estimated from well-trained evidential deep learning networks better reflects model performance. To this end, we propose a novel uncertainty-guided class imbalance learning framework - UCLSED, and its variant - UCL-ECSED, for imbalanced social event detection tasks. We aim to improve the overall model performance by enhancing model generalization to those uncertain classes. Considering performance degradation usually comes from misclassifying samples as their confusing neighboring classes, we focus on boundary learning in latent space and classifier learning with high-quality uncertainty estimation. First, we design a novel uncertainty-guided contrastive learning loss, namely UCL and its variant - UCL-EC, to manipulate distinguishable representation distribution for imbalanced data. During training, they force all classes, especially uncertain ones, to adaptively adjust a clear separable boundary in the feature space. Second, to obtain more robust and accurate class uncertainty, we combine the results of multi-view evidential classifiers via the Dempster-Shafer theory under the supervision of an additional calibration method. We conduct experiments on three severely imbalanced social event datasets including Events2012_100, Events2018_100, and CrisisLexT_7. Our model significantly improves social event representation and classification tasks in almost all classes, especially those uncertain ones. Copyright © 2023, The Authors. All rights reserved.

关键词： Uncertainty analysis

NTIRE 2023 Challenge on Efficient Super-Resolution: Methods and Results

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NTIRE 2023 Challenge on Efficient Super-Resolution: Methods ...

2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Li, Yawei Zhang, Yulun Timofte, Radu Van Gool, Luc Yu, Lei Li, Youwei Li, Xinpeng Jiang, Ting Wu, Qi Han, Mingyan Lin, Wenjie Jiang, Chengzhi Luo, Jinting Fan, Haoqiang Liu, Shuaicheng Wang, Yucong Cai, Minjie Li, Mingxi Zhang, Yuhang Fan, Xian-Jun Sheng, Yankai Mao, Yanyu Zhang, Nihao Wang, Qian Zheng, Mingjun Sun, Long Pan, Jinshan Dong, Jiangxin Tang, Jinhui Yang, Zhongbao Wang, Yan Pan, Erlin Cai, Qixuan Dai, Xinan Zhussip, Magauiya Kalyazin, Nikolay Vyal, Dmitry Zou, Xueyi Yan, Youliang Chung, Heaseo Zhang, Jin Yu, Gaocheng Zhang, Feng Wang, Hongbin Liao, Bohao Du, Zhibo Wu, Yu-Liang Shi, Gege Peng, Long Wang, Yang Cao, Yang Zha, Zhengjun Huang, Zhi-Kai Chen, Yi-Chung Chiang, Yuan-Chun Yang, Hao-Hsiang Chen, Wei-Ting Chang, Hua-En Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Liu, Xin Pan, Jiahao Yu, Hongyuan Yu, Weichen Ge, Lin Dong, Jiahua Zou, Yajun Wu, Zhuoyuan Han, Binnan Zhang, Xiaolin Zhang, Heng Yin, Xuanwu Zuo, Kunlong Deng, Weijian Yuan, Hongjie Lu, Zengtong Ouyang, Mingyu Ma, Wenzhuo Liu, Nian Zheng, Hanyou Zhang, Yuantong Zhang, Junxi Chen, Zhenzhong Gendy, Garas Sabor, Nabil Hou, Jingchao He, Guanghui Zhu, Yurui Wang, Xi Fu, Xueyang Zha, Zheng-Jun Yin, Daheng Liu, Mengyang Chen, Baijun Li, Ao Luo, Lei Jin, Kangjun Zhu, Ce Zhang, Xiaoming Xie, Chengxing Li, Linze Meng, Haiteng Zhang, Tianlin Li, Tianrui Zhao, Xiaole Zhang, Zhao Li, Baiang Zheng, Huan Zhao, Suiyi Gao, Yangcheng Ren, Jiahuan Hu, Kang Shi, Jingpeng Wu, Zhijian Huang, Dingjiang Zhu, Jinchen Li, Hui Xv, Qianru Liu, Tianle Weng, Shizhuang Wu, Gang Jiang, Junpeng Liu, Xianming Jiang, Junjun Zhang, Mingjian Hu, Jing Wu, Chengxu Fan, Qinrui Feng, Chengming Luo, Ziwei Hu, Shu Lyu, Siwei Wu, Xi Wang, Xin Computer Vision Lab ETH Zurich Switzerland Computer Vision Lab University of Würzburg Germany Megvii Technology China MicroBT China College of Computer Science and Electronic Engineering Hunan University China Attrsense Xian University of Posts and Telecommunications Xi'an China National Engineering Laboratory for Cyber Event Warning and Control Technologies China Nanjing University of Science and Technology China Nankai University China University of Electronic Science and Technology of China China Tianjin University China Noah's Ark Lab Huawei Technologies McMaster University Canada Espresomedia Korea Republic of AntGroup China University of Science and Technology of China China Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Graduate Institute of Electronics Engineering National Taiwan University Taiwan ServiceNow United States China Mobile Research Institute China Chongqing University of Technology China Multimedia Department Xiaomi Inc. China Institute of Automation Chinese Academy of Sciences China Shenyang Institute of Automation Chinese Academy of Sciences China School of Information and Communication Engineering Communication University of China China Smart Classroom Division Ruijie Networks Co. Ltd. China School of Remote Sensing and Information Engineering Wuhan University China Micro-Nano Electronics Department Shanghai Jiao Tong University Shanghai200240 China Electrical Engineering Department Faculty of Engineering Assiut University Assiut71516 Egypt Southeast University China University of Electronic Science and Technology of China Chengdu China Chongqing University of Posts and Telecommunications Chongqing China Information Technology Co. Ltd Hangzhou China Southwest Jiaotong University China National Space Science Center Chinese Academy of Science China Hefei University of Technology Hefei China Anhui University China Fried Ric

ISBN: (纸本)9798350302493

This paper reviews the NTIRE 2023 challenge on efficient single-image super-resolution with a focus on the proposed solutions and results. The aim of this challenge is to devise a network that reduces one or several aspects such as runtime, parameters, FLOPs, activations, memory footprint, and depth of RFDN while at least maintaining the PSNR of 29.00dB on DIV2K validation datasets. The challenge had 272 registered participants, and 35 teams made valid submissions. They gauge the state-of-the-art for efficient single-image super-resolution. © 2023 IEEE.

关键词： Optical resolving power

Rotation Speed and Wind Speed Indirect Measurement Methods for the Control of Windmills with Fixed Blades Turbine

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Rotation Speed and Wind Speed Indirect Measurement Methods f...

2008 IEEE Instrumentation and Measurement Technology Conference (I2MTC 2008), vol.3

作者： N. Budisan V. Groza O. Prostean I. Filip M. Biriescu I. Szeidert M. Stern Faculty of Automation and Computer Science and Engineering Polytechnic University of Timisoara Timisoara Romania School of Information Technology and Engineering (SITE) University of Ottawa Ottawa ONT Canada Cleanfield Energy Corporation Ancaster ONT Canada

The implementation complexity of classical algorithms for wind turbines control limits the utilization of low-end microcontrollers for such applications. We conceived an original method that aims to optimize the efficiency of windmills with fixed blades with the variation of the wind speed. In the paper we present an original method to determine generator's efficiency values using measurements that were performed at the prototype's homologation, along with original simplified models of wind turbine and of wind turbine - generator assembly. Measured values of the frequency, voltage and current of the generator are applied to determine the turbine rotation speed and the wind speed, reducing thus the number of needed sensors. A couple of families of regression functions were derived and tuned accordingly to the factory experimental characteristics to allow for a facile calculation of the generator's efficiency database table. To further speedup the assessment of the optimum/safety value of the windmill rotation speed, the generator's model is considered to provide the rotation as a function of the wind speed in a tabular format that is further used at run-time as a look-table for rapid interpolations in the control loop. The main advantages of the proposed approach are the simplicity and robustness of the implementation, being easily portable to low-end microcontroller platforms.

关键词： indirect measurement methods renewable energy fixed blades turbine windmills control

Medical Visual Prompting (MVP): A Unified Framework for Versatile and High-Quality Medical Image Segmentation

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arXiv 2024年

作者： Chen, Yulin Huang, Guoheng Huang, Kai Lin, Zijin Zhong, Guo Luo, Shenghong Deng, Jie Zhou, Jian School of Automation Guangdong University of Technology Guangzhou China School of Computer Science and Technology Guangdong University of Technology Guangzhou China School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou China Faculty of Science and Technology University of Macau China Department of Otorhinolaryngology The First Affiliated Hospital of Sun Yat-Sen University Guangzhou China Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Sun Yat-sen University Cancer Center Guangzhou China South China Hospital Shenzhen University Shenzhen China

Accurate segmentation of lesion regions is crucial for clinical diagnosis and treatment across various diseases. While deep convolutional networks have achieved satisfactory results in medical image segmentation, they face challenges such as loss of lesion shape information due to continuous convolution and downsampling, as well as the high cost of manually labeling lesions with varying shapes and sizes. To address these issues, we propose a novel medical visual prompting (MVP) framework that leverages pre-training and prompting concepts from natural language processing (NLP). The framework utilizes three key components: Super-Pixel Guided Prompting (SPGP) for superpixelating the input image, Image Embedding Guided Prompting (IEGP) for freezing patch embedding and merging with superpixels to provide visual prompts, and Adaptive Attention Mechanism Guided Prompting (AAGP) for pinpointing prompt content and efficiently adapting all layers. By integrating SPGP, IEGP, and AAGP, the MVP enables the segmentation network to better learn shape prompting information and facilitates mutual learning across different tasks. Extensive experiments conducted on five datasets demonstrate superior performance of this method in various challenging medical image tasks, while simplifying single-task medical segmentation models. This novel framework offers improved performance with fewer parameters and holds significant potential for accurate segmentation of lesion regions in various medical tasks, making it clinically valuable. Copyright © 2024, The Authors. All rights reserved.

关键词： Superpixels