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Fuzzy identification of nonlinear dynamic system based on selection of important input variables

Journal of systems engineering and Electronics 2022年第3期33卷 737-747页

作者： LYU Jinfeng LIU Fucai REN Yaxue Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment Yanshan UniversityQinhuangdao 066004China School of Mathematics and Information Science and Technology Hebei Normal University of Science and TechnologyQinhuangdao 066004China

Input variables selection(IVS) is proved to be pivotal in nonlinear dynamic system modeling. In order to optimize the model of the nonlinear dynamic system, a fuzzy modeling method for determining the premise structure by selecting important inputs of the system is studied. Firstly, a simplified two stage fuzzy curves method is proposed, which is employed to sort all possible inputs by their relevance with outputs, select the important input variables of the system and identify the ***, in order to reduce the complexity of the model, the standard fuzzy c-means clustering algorithm and the recursive least squares algorithm are used to identify the premise parameters and conclusion parameters, respectively. Then, the effectiveness of IVS is verified by two well-known issues. Finally, the proposed identification method is applied to a realistic variable load pneumatic system. The simulation experiments indi cate that the IVS method in this paper has a positive influence on the approximation performance of the Takagi-Sugeno(T-S) fuzzy modeling.

关键词： Takagi-Sugeno(T-S)fuzzy modeling input variable selection(IVS) fuzzy identification fuzzy c-means clustering algorithm

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A Polarimetric Target Decomposition Algorithm for Grassland Area with Orthogonal Scattering Model 2

A Polarimetric Target Decomposition Algorithm for Grassland ...

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2nd IEEE International Conference on Signal, information and Data Processing, ICSIDP 2024

作者： Chen, Yalan Huang, Pingping Li, Xiujuan Yang, Xiangli Wang, Zhiguo Dong, Yifan Inner Mongolia Key Laboratory of Radar Technology and Application College of Information Engineering Inner Mongolia University of Technology Hohhot China Chongqing Jiaotong University School of Information Science and Engineering Chongqing China

ISBN: (纸本)9798331515669

As an essential component of terrestrial ecosystems, grassland ecosystems cover a large portion of the Earth's surface and provide significant benefits, including soil and water conservation as well as biodiversity preservation. They play an irreplaceable role in maintaining ecological balance and supporting human society. However, among the existing polarimetric decomposition algorithms, there is a lack of decomposition algorithms built for grassland characteristics. To address this problem, this paper proposes a polarimetric SAR data decomposition algorithm based on two mutually orthogonal rank-1 scattering models, enabling better separation of the corresponding scattering mechanisms. On this basis, the commonly used uniformly oriented dipole model is replaced with a volume scattering model of dual dominant-oriented dipoles, tailored to the characteristics of grassland structures, such as houses. This adjustment results in decomposition outcomes that are more consistent with real-world scenarios. Experimental results demonstrate that the proposed method not only provides more accurate decomposition results but also has the advantage of reducing negative power components. © 2024 IEEE.

关键词： Abiotic

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Optimization of radar reconnaissance constellation based on fuzzy relative entropy 6

Optimization of radar reconnaissance constellation based on ...

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6th International Conference on Advanced Electronic Materials, Computers, and Software engineering, AEMCSE 2023

作者： Liu, Yali Xiong, Wei Han, Chi Science and Technology on Complex Electronic System Simulation Laboratory Space Engineering University Beijing101416 China

ISBN: (纸本)9781510668249

The optimization of radar reconnaissance constellation is of great significance for reconnaissance timeliness. In order to solve the problems of insufficient selection pressure and poor diversity of the evolutionary algorithm based on Pareto domination in solving the many-objective optimization problem of radar reconnaissance constellation, an imporved particle swarm optimization based on fuzzy relative entropy (IFREM_PSO) is proposed. A new inertial weight strategy is proposed to enhance the convergence speed and accuracy of the algorithm, improve the external archive maintenance strategy, and enhance the convergence and diversity of the algorithm. The optimization model of radar imaging reconnaissance constellation oriented to area reconnaissance is constructed. The IFREM_PSO is used to optimize the model, and the multi-objective particle swarm optimization algorithm (MOPSO) is used as the control algorithm. Experiments show that the IFREM_PSO has better results in terms of convergence, diversity and convergence speed, and can effectively solve the problem of radar reconnaissance constellation optimization. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Entropy

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Visual observation of photonic Floquet–Bloch oscillations

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Light(science & Applications) 2024年第5期13卷 921-929页

作者： Zhen Zhang Yuan Li Xiankai Sun Xuewen Shu Wuhan National Laboratory for Optoelectronics and School of Optical and Electronic Information Huazhong University of Science and TechnologyWuhanHubei 430074China Department of Electronic Engineering The Chinese University of Hong KongShatinNew TerritoriesHong Kong SARChina

Bloch oscillations(BOs),an important transport phenomenon,have been studied extensively in static systems but remain mysterious in Floquet ***,by harnessing notions from photonic analogy,we propose a generalization of the existing BOs in photonic Floquet lattices,namely the“photonic Floquet–Bloch oscillations”,which refer to rescaled photonic Bloch oscillations with a period of extended least common multiple of the modulation period and the Bloch oscillation ***,we report the first visual observation of such photonic Floquet–Bloch oscillations(FBOs)by employing waveguide fluorescence *** significantly,the FBOs surpass the existing BOs in Floquet systems and exhibit exotic properties on their own,including fractal spectrum and fractional Floquet *** new transport mechanism offers an intriguing method of wave manipulation that may contribute to rapidly developing fields in photonics,condensed matter physics,and quantum physics.

关键词： Bloch analogy generalization

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A grapefruit microstructure fiber temperature sensor coated with liquid crystal based on waist-enlarged taper

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Optoelectronics Letters 2024年第3期20卷 142-146页

作者： WANG Feng LI Jiaxuan ZHANG Rongjing FU Xinghu College of Mechanical and Electrical Engineering Hebei Normal University of Science&TechnologyQinhuangdao 066004China School of Information Science and Engineering Key Laboratory for Special Fiber and Fiber Sensor of Hebei ProvinceYanshan UniversityQinhuangdao 066004China

In this paper, a grapefruit microstructure fiber(GMF) temperature sensor coated with liquid crystal(LC) based on waist-enlarged taper is proposed and fabricated, and its temperature sensing characteristics are analyzed. The waist-enlarged taper is formed at the fusion point between single mode fiber(SMF) and GMF. The capillary glass tube is sleeved outside GMF, LC is filled into the capillary glass tube, and its two ends are finally sealed to form a sensor. The experimental results show that when the length of GMF is 2.5 cm, the temperature sensitivity of the sensor can reach up to 195.3 pm/℃ in the range of 30-90℃, and it has a good stability for reuse. Thereby, it can be used in biochemical, industrial production and other temperature detection areas.

关键词： capillary fiber microstructure

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CRFENet: Cross-Spectral Stereo Matching Network Exploiting Reciprocal Feature Evolution 2nd

CRFENet: Cross-Spectral Stereo Matching Network Exploiting R...

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2nd International Conference on the Frontiers of Robotics and Software engineering, FRSE 2024

作者： Li, Yangqin Zhang, Guanghui Zhu, Dongchen Shi, Wenjun Wang, Lei Zhang, Xiaolin Shanghai Institute of Microsystem and Information Technology Bionic Vision System Laboratory State Key Laboratory of Transducer Technology Chinese Academy of Sciences Shanghai200050 China University of Chinese Academy of Sciences Beijing100049 China School of Information Science and Technology ShanghaiTech University Shanghai201210 China

ISBN: (纸本)9789819630127

Cross-spectral stereo matching opens up a new route for disparity estimation in low-light scenes. However, the variations in light absorption and reflection properties for objects at different wavelengths create substantial domain gaps in cross-spectral image pairs. Accurately matching pixel pairs with significant domain gaps remains challenging. In this work, we propose a novel lightweight cross-spectral stereo matching network, dubbed CRFENet, exploiting reciprocal feature evolution. It enables the complementary evolution of the disparity prediction network and spectral translation network. To facilitate feature interactions between different domains to improve disparity results in regions with significant domain gaps, we design a domain mutual rail (DMR) module, which integrates monocular spectral and binocular cross-spectral features. Furthermore, we present a domain gap consistency constraint and corresponding confidence-weighted loss function, which aim to alleviate the matching challenge in regions with significant domain gaps through decoupling domain gap and disparity. Experimental results indicate that our CRFENet achieves state-of-the-art performance on the PittsStereo RGB-NIR and DrivingStereo datasets, especially for challenging regions with significant domain gaps. Besides, the model parameters are only 3.6M, which means high practicality. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Stereocenters

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Double Acceleration of Distributed Nesterov Gradient Descent Algorithms by Exploiting Momentum 22

Double Acceleration of Distributed Nesterov Gradient Descent...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Yi, Xin Yi, Jing-Wen Chai, Li School of Information Science and Engineering Wuhan University of Science and Technology Wuhan430081 China State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China

ISBN: (纸本)9798331527471

It is well known that adding the momentum term can accelerate the convergence rate of optimization algorithms. In this paper, the momentum term is exploited to double accelerate the distributed Nesterov gradient descent algorithm without increasing the communication burden, and a novel momentum-based distributed Nesterov gradient descent (DNGD-M) algorithm is proposed. Using the method of inequality transformation, the convergence problem of the DNGD-M algorithm is converted into the stability problem of the extended system. Then, by analyzing the state transition matrix of the extended system, the convergence conditions of the proposed algorithm and the corresponding ranges of parameter values are obtained. Finally, numerical experiments are provided to verify the effectiveness of the proposed algorithm. © 2024 IEEE.

关键词： Optimization algorithms

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Multimodal Evidential Learning for Open-World Weakly-Supervised Video Anomaly Detection

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IEEE Transactions on Multimedia 2025年 27卷 3132-3143页

作者： Huang, Chao Huang, Weiliang Jiang, Qiuping Wang, Wei Wen, Jie Zhang, Bob Sun Yat-sen University School of Cyber Science and Technology Shenzhen Campus Shenzhen518000 China University of Macau PAMI Research Group Department of Computer and Information Science 999078 China Ningbo University Faculty of Information Science and Engineering Ningbo315211 China Harbin Institute of Technology Shenzhen Key Laboratory of Visual Object Detection and Recognition Shenzhen518055 China

Efforts in weakly-supervised video anomaly detection center on detecting abnormal events within videos by coarse-grained labels, which has been successfully applied to many real-world applications. However, a significant limitation of most existing methods is that they are only effective for specific objects in specific scenarios, which makes them prone to misclassification or omission when confronted with previously unseen anomalies. Relative to conventional anomaly detection tasks, Open-world Weakly-supervised Video Anomaly Detection (OWVAD) poses greater challenges due to the absence of labels and fine-grained annotations for unknown anomalies. To address the above problem, we propose a multi-scale evidential vision-language model to achieve open-world video anomaly detection. Specifically, we leverage generalized visual-language associations derived from CLIP to harness the full potential of large pre-trained models in addressing the OWVAD task. Subsequently, we integrate a multi-scale temporal modeling module with a multimodal evidence collector to achieve precise frame-level detection of both seen and unseen anomalies. Extensive experiments on two widely-utilized benchmarks have conclusively validated the effectiveness of our method. The code will be made publicly available. © 1999-2012 IEEE.

关键词： Self-supervised learning

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Multi-objective optimization of reconnaissance constellation based on machine learning 4

Multi-objective optimization of reconnaissance constellation...

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4th International Conference on Computer science and Communication technology, ICCSCT 2023

作者： Liu, Yali Xiong, Wei Xiong, Minghui Yu, Xiaolan Liu, Zheng Science and Technology on Complex Electronic System Simulation Laboratory Space Engineering University Beijing101416 China

ISBN: (纸本)9781510671232

The optimization of reconnaissance constellation is an important step in the construction of space-based information system, and the coverage performance is the key index of the optimization of reconnaissance constellation. However, the coverage performance of the current constellation is mostly obtained through the simulation system, which has high time complexity and low efficiency, seriously slowing down the speed of constellation optimization. In order to improve the efficiency of constellation optimization, a two-stage optimization scheme of reconnaissance constellation based on machine learning is proposed. First, the trained machine learning prediction model is combined with the evolutionary algorithm to carry out rough optimization of the reconnaissance constellation, and the decision variable space after the scope is reduced is obtained. Then, in the reduced decision variable space, the simulation system and evolutionary algorithm are used to further optimize the problem, and the final result is obtained. The scheme proposed in this paper speeds up the convergence of population by reducing the search space of decision variables, thus improving the optimization efficiency. By comparing the optimization results of this scheme with those of the traditional scheme, it is found that the proposed scheme is 78.21% shorter than that of the traditional scheme under the premise that the quality gap between the optimization results of the traditional scheme and the traditional scheme is acceptable, which greatly improves the optimization speed of the reconnaissance constellation, and verifies the effectiveness and advancement of the proposed scheme. © 2023 SPIE.

关键词： Multiobjective optimization

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SPSI:A Novel Composite Index for Estimating Panicle Number in Winter Wheat before Heading from UAV Multispectral Imagery

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Plant Phenomics 2023年第3期5卷 642-656页

作者： Yapeng Wu Wenhui Wang Yangyang Gu Hengbiao Zheng Xia Yao Yan Zhu Weixing Cao Tao Cheng National Engineering and Technology Center for Information Agriculture(NETCIA) MARA Key Laboratory for Crop System Analysis and Decision MakingMOE Engineering Research Center of Smart AgricultureJiangsu Key Laboratory for Information AgricultureNanjing Agricultural UniversityOne WeigangNanjingJiangsu 210095PR China Langfang Normal University 100 Aimin West RoadLangfangHebei 065000PR China

Rapid and accurate estimation of panicle number per unit ground area(PNPA)in winter wheat before heading is crucial to evaluate yield potential and regulate crop growth for increasing the final *** accuracies of existing methods were low for estimating PNPA with remotely sensed data acquired before heading since the spectral saturation and background effects were *** study proposed a spectral-textural PNPA sensitive index(SPSI)from unmanned aerial vehicle(UAV)multispectral imagery for reducing the spectral saturation and improving PNPA estimation in winter wheat before *** effect of background materials on PNPA estimated by textural indices(TIs)was examined,and the composite index SPSI was constructed by integrating the optimal spectral index(SI)and ***,the performance of SPSI was evaluated in comparison with other indices(SI and TIs).The results demonstrated that green-pixel TIs yielded better performances than all-pixel TIs apart from TI_([HOM]),TI_([ENT]),and TI_([SEM])among all indices from 8 types of textural ***,which was calculated by the formula DATT_([850,730,675])+NDTICOR_([850,730]),exhibited the highest overall accuracies for any date in any dataset in comparison with DATT_([850,730,675]),TINDRE_([MEA]),and NDTICOR_([850,730]).For the unified models assembling 2 experimental datasets,the RV^(2) values of SPSI increased by 0.11 to 0.23,and both RMSE and RRMSE decreased by 16.43%to 38.79%as compared to the suboptimal index on each *** findings indicated that the SPSI is valuable in reducing the spectral saturation and has great potential to better estimate PNPA using high-resolution satellite imagery.

关键词： heading Wheat indices

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